#+TITLE: 2 Higgs - SU(2) Simulations Compute Averages, Flow Averages, Scale Setting, Plaquette, Clover,... Study phase space with Higgs and W masses #+startup: num #+STARTUP: latexpreview #+LaTeX_HEADER: \usepackage{pdfpages} #+LATEX_HEADER: \usepackage{physics} #+LATEX_HEADER: \usepackage{mathtools} * Log ** SCHEDULED: <2023-05-15 Mon> - The simulations done at k1=k2 indicate that the phase transition occurs at the same k_c for both cases (bet = 6.0; et1=0.01; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01) - It also indicates that the W mass obtained with H1 and H2 starts to separate for lower k1=k2 in the symmetric phase - this means that W_{11}=W_{22} indicates we are safely in the broken phase - The next step is to keep one of the kappas fixed in the broken phase (where W_{11}=W_{22}) and scan the other kappa - we want to gauge the possibility of having one of the higgs broken and the other not ** SCHEDULED: <2023-05-16 Tue> - The simulations done at; + beta = 6.0; et1=0.01; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 show mW > mH. - It seems that mW2 ($\eta_2>\eta_1$) is slightly larger than mW1 (this shouldn't happen ? both interpolators should return the same mass ? how is this possible ?) - [Attempt] keep $\mu$ and the $\xi$ fixed - change $\eta_2$ see how the W mass changes with this coupling. Keep all parameters unchanged (for k_c to remain the same) - Maybe I should've set xi1=xi2=xi3=xi4 since all these terms mix both Higgs ** SCHEDULED: <2023-05-17 Wed> - From yesterday's idea, it seems that the ratio mH/mW grows when $\eta_2$ increases, so it should be possible to reverse the mass ordering ** SCHEDULED: <2023-05-18 Thu> - As a test, I also checked the mass dependence on $\eta_1$. The behaviour seems to indicate that to reverse the orderings we need to increase also $\eta_1$ at a fixed $\eta_2$. However, when it increases too much it seems that $m_{W_1}$ becomes different from $m_{W_2}$, which indicates a phase transition (?) However, for this beta and k1=k2 the k_crit should be far away. Could the eta change k_crit that much? - ver fig. 4.18 & 4.14 Wurtz thesis - This seems to be confirmed by plotting also the value of $L_{\alpha}$ that DECREASES with $\eta_1$ and thus indicates that were crossing to the symmetric phase - Then, if the change in the masses and mass ratios with the $\eta$ are due to getting close to the phase transition, this means that the mass ordering will only approach 1 (when all masses are zero) but not become larger than one in the broken phase - Reminder that all this is for k1=k2. It should be possible to reverse the mass ordering by changing the ks. However, it is puzzling that for k1=k2 we get an inverse mass ordering from the Single Higgs case. ** SCHEDULED: <2023-05-19 Fri> - Changing $\eta_1$ I confirmed that we simply change our fixed $k$ to be closer to the new $k_c=k_c(\eta,\xi,...)$ - the way to reverse the mass ordering is by tuning the parameters using the masses obtained by the Higgs mechanics applied to our action ** SCHEDULED: <2023-05-23 Tue> - Should redo smearing tests by plotting as a function of $N\hat r$ and not only $N$ - For k1=k2 both Higgs are in the same phase all the time because the quartic couplings affect $k_c^{(i)}$ very little. Thus, for k1=k2 the W-boson receives contributions from both vevs in the broken phase, this means that it gets more massive. To get the *correct mass order* we should find a region where only one Higgs is in the broken phase. However, also the remaining Higgs (after SSB) gets its mass from the sum of both VEVs... So I should be still able to find mH>mW even when both Higgs are broken... - From Branco's review I noticed that the scalar masses are all proportional to the coupling $\mu^2$ at tree level. It means that by changing its value we can make the Higgs mass larger: Run scan for $\mu^2$ - [EDIT] by increasing $\mu$ the ratio mH/mW grows and approaches 1. However, it seems to stabilize around one even if one increases $\mu$ furter (keeping all other couplings constant) - Notice also that Branco's reviews and the other papers assume always that both Higgs are broken. This is not the most general case. It is possible that one is broken and the other is not. What are the conditions on the parameters for this? For the single Higgs this means $m^2<0$ Require minimization problem? - In the single higgs case, when the symmetry is broken there are 3 goldstone bosons that are eaten by the Ws and Z, otherwise they would remain massless. This would be due to the fact that we can still rotate the choice of vacuum in 3 different ways (mexican hat potential corresponds to 1 massless direction). if this would be the case it would mean that we would have 3 massless states in the theory - this could be checked, and we would have large FV effects. Does this mean that in the 2HDM if 3 dofs are eaten by the Ws and Z there remain massless directions? I don't think so because the massless directions come from remaining symmetries of the Lagrangian even after choosing the vacuum. *** TODO understand this ** SCHEDULED: <2023-05-24 Wed> - I think it makes little sense to wander through this model at random, without any analytical idea. I will focus on a simpler case - no CP violation terms and no Z2 violation terms - For now I will follow [cite:@deshpande_pattern_1978]. This is called in [cite:@ivanov_algorithmic_2018] the Inert Doublet model. - Smaller quartic couplings - same order as for single higgs - inequalities with lattice couplings ** SCHEDULED: <2023-05-29 Mon> - Keeping k1=0.133 fixed and scanning k2 seems to show that the m1 masses are constant (shouldnt be totally constant due to renormalization) while m2 changes and goes through the phase transition - Im scanning k2 for a range that is too large - need to go closer to PH + Looking at the previous scans, *k_c ~0.1322* (beta=6.0 and other quartic couplings, but shouldnt change much) + Going to scan k2 around this value - Looking at the global observables it is noticeable that Lalpha=const for Higgs1 - Also, now mH > mW for the whole range of k2 - *[NOTE]* now the W-Mass extracted with H2 is very large meaning that the 'W' interpolator built with $\phi_2$ is coupling to another state that is not the W - this also means that for the whole k2 range is still in phase (C) + need larger k2 ** SCHEDULED: <2023-05-30 Tue> - W12 seems to show some good signal close to the phase transition for k1 dif. than k2 ??? + *tem massa muito pequena e aparentementa constante com k2 - GOLDSTONE???* ** SCHEDULED: <2023-06-02 Fri> - No scan de k2 com k1 fixo a transição de k2 sym.-> broken, a massa mW2 não vai a zero em nenhum momento. No entanto mH2 sim. - Mesmo com a massa de mH2 a ir perto de zero, nunca tive problemas de termalização como ocorre na transição de fase com k1=k2 [edit] W12 mostra problemas grandes de termalização por ser tão leve. - O interpoladores de W1 e W2 oscilam em torno de zero em todo o espaço de parametros. Por outro lado, W12 oscila em torno de zero quando k2 < kc, mas quando ambos os Higgs são quebrados, W12 passa a oscilar em torno de um valor dif. zero (negativo ou positivo dependendo da Run, apesar de não ter detetado saltos entre os dois vácuos). Isto é uma *quebra de Z2* - mas porquê? - Nos estamos a testar o Inert Higgs Model de [cite:@deshpande_pattern_1978]. O que estou a ver é que na fase (C), onde $\expval{\phi_1}\neq0,~\expval{\phi_2}=0$, observa-se $\expval{\phi_1^\dagger\phi_2}\neq 0$. A simetria $\mathbb{Z}_2$ associada a phi2 mantém-se. Na fase (D) esta Z2 também é quebrada. Assim, mesmo não tendo os termos no lagrangiano que quebram explicitamente $\mathbb{Z}_2\times\mathbb{Z}_2$, esta é quebrada totalmente na fase (D) - Para continuar as runs, vou ligar os couplings $\mu,\xi_3,\xi_4$ e assumir que não afetam muito k_c. Estes couplings quebram explicitamente Z2xZ2 - Finally convinced David to look at the definitions of the terms in the lagrangian - we should put back $\lambda_5$ ** SCHEDULED: <2023-06-05 Mon> - See section 5.10 [cite:@branco_theory_2012-1] - Mass matrix for Neutral minima - Inert ** SCHEDULED: <2023-06-19 Mon> - I've been trying to understand how to write the 2HDM using the quaternion formulation to match the usual doublet formulation used in the continuum literature. What seems to be the question right now is: what is the global symmetry that is broken in the Higgs mechanism? Is it the global gauge SU(2), or is it a global O(4) associated with the four components of the higgs? If its the latter, our potential should always have an O(4) symmetry that is not broken explicitly Reminder: SU(2)xSU(2) ~ O(4) - Should read Axel Maas review! ** SCHEDULED: <2023-06-26 Mon> - As runs com + bet = 5.5; k1=0.133; et1=0.003; et2=0.001; mu=0.001; xi1=0.0001; xi2=0.0001; xi3=0.0005; xi4=0.0001 parecem ter o K_crit num valor diferente das runs anteriores com + bet = 5.5; k1=0.133; et1=0.003; et2=0.001; mu=0.0; xi1=0.0001; xi2=0.0001; xi3=0.0; xi4=0.0 - Vou lançar k1=k2 scan com os mesmos valores dos quartic couplings para estimar k_crit ** SCHEDULED: <2023-06-27 Tue> - the transition doesnt seem to happen anywhere in the region 0.11-0.13 but for larger values of k1=k2 - launched a new simulation for larger k1=k2 values ** SCHEDULED: <2023-06-29 Thu> - As simulações com k1=k2 com L=16 aparentam ter k_crit > 0.131 - No entanto, as simulações com k1=0.133 fixo estão todas na fase (D) - Pode ser o volume? ou o facto de ter k1 dif. de k2? ** SCHEDULED: <2023-06-30 Fri> - A transição entre a fase (C) para (D) com a presença de todos os termos tem a transição para k_crit mais baixo do que sem os termos (mu,eta6,eta7) (Convenção antiga - eta4=eta5 continuum bet = 5.5; k1=0.133; et1=0.003; et2=0.001; mu=0.001; xi1=0.0001; xi2=0.0001; xi3=0.0005; xi4=0.0001) - Além disso, a transição entre estas duas fases parece ser mais suave * Functions ** Packages #+begin_src jupyter-julia :session h2ps cd("/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/") print(pwd(),"\n\n") import Pkg Pkg.activate("/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/latticegpu.jl") using Plots, MTH229, LaTeXStrings, Latexify,PlotThemes, ColorSchemes,Printf,LsqFit,BDIO, ADerrors, Colors, LatticeGPU Pkg.status() global markers = [:rect ,:circle,:utriangle, :xcross , :cross] global msize = [4, 4, 3, 5, 5] #+end_src #+RESULTS: #+begin_example /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs [36m[1m Project[22m[39m LatticeGPU v0.1.0 [32m[1m Status[22m[39m `~/PhD/LatGPU/SU2proj/code/runs_su2higgs/latticegpu.jl/Project.toml` [90m [5e92007d] [39mADerrors v0.1.0 `https://igit.ific.uv.es/alramos/aderrors.jl#master` [90m [375f315e] [39mBDIO v0.1.0 `https://gitlab.ift.uam-csic.es/alberto/bdio.jl.git#master` [90m [052768ef] [39mCUDA v3.6.2 [90m [09ab397b] [39mStructArrays v0.6.15 [90m [a759f4b9] [39mTimerOutputs v0.5.13 [90m [9a3f8284] [39mRandom [90m [fa267f1f] [39mTOML [32m[1m Activating[22m[39m project at `~/PhD/LatGPU/SU2proj/code/runs_su2higgs/latticegpu.jl` ┌ Info: Precompiling LatticeGPU [958c3683-801a-4582-9cfa-2d6e2ae1763b] └ @ Base loading.jl:1423 #+end_example ** Functions - Read Simulation Parameters/Data #+begin_src jupyter-julia :session h2ps function readparams(fb) #move read position to the first record BDIO_seek!(fb,0) let nth, niter, NSC, flw_steps, flw_dtr, flw_iter, sus, sss, beta, k1, k2, eta1, eta2, mu, xi1, xi2, xi3, xi4, flw_dt, sdt, srs while BDIO_seek!(fb) # Simulation parameters if BDIO_get_uinfo(fb) == 1 int_array = similar(Array{Int64, 1}, 8) BDIO_read(fb, int_array) nth = int_array[1] niter = int_array[2] NSC = int_array[3] flw_steps = int_array[4] flw_dtr = int_array[5] flw_iter = int_array[6] sus = int_array[7] sss = int_array[8] end if BDIO_get_uinfo(fb) == 2 flt_array = similar(Array{Float64, 1}, 13) BDIO_read(fb, flt_array) beta = flt_array[1] k1 = flt_array[2] k2 = flt_array[3] eta1 = flt_array[4] eta2 = flt_array[5] mu = flt_array[6] xi1 = flt_array[7] xi2 = flt_array[8] xi3 = flt_array[9] xi4 = flt_array[10] flw_dt = flt_array[11] sdt = flt_array[12] srs = flt_array[13] end end gp = GaugeParm{Float64}(SU2{Float64}, beta, 1.0) sp = ScalarParm((k1,k2), (eta1,eta2), mu, (xi1, xi2, xi3, xi4)) return nth, niter, flw_steps, flw_dtr, flw_iter, sus, sss, flw_dt, gp, sp, sdt, srs end end function readdata(fb,nth, niter, flw_iter, flw_steps) #move read position to the first record k_index = 0 BDIO_seek!(fb,0) let pl, rho1, rho2, Lphi1, Lphi2, Lalp1, Lalp2, dh, flwtime, Echain, Eclchain, h2, w1r, w1i flwtime = Vector{Float64}(undef, flw_steps) Echain = Array{Float64,2}(undef, flw_iter, flw_steps) Eclchain = Array{Float64,2}(undef, flw_iter, flw_steps) h2 = Array{Float64,3}(undef, 2, tim, niter) w1 = Array{Float64,5}(undef, 3, 3, 3, tim, niter) while BDIO_seek!(fb) #observables #Plaquette if BDIO_get_uinfo(fb) == 3 k_index += 1 db_array = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array) pl = db_array end # Rho if BDIO_get_uinfo(fb) == 4 db_array1 = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array1) rho1 = db_array1 db_array2 = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array2) rho2 = db_array2 end # Lphi if BDIO_get_uinfo(fb) == 5 db_array1 = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array1) Lphi1 = db_array1 db_array2 = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array2) Lphi2 = db_array2 end # Lalp if BDIO_get_uinfo(fb) == 6 db_array1 = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array1) Lalp1 = db_array1 db_array2 = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array2) Lalp2 = db_array2 end #dh if BDIO_get_uinfo(fb) == 8 db_array = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array) dh = db_array end # FLOW # Flow time if BDIO_get_uinfo(fb) == 9 db_array = similar(Array{Float64, 1}, flw_steps) BDIO_read(fb, db_array) flwtime = db_array end #Flow E if BDIO_get_uinfo(fb) == 10 db_array = similar(Array{Float64, 1}, flw_iter) for i in 1:flw_steps BDIO_read(fb, db_array) Echain[:,i] .= db_array end end #Flow E - Clover if BDIO_get_uinfo(fb) == 11 db_array = similar(Array{Float64, 1}, flw_iter) for i in 1:flw_steps BDIO_read(fb, db_array) Eclchain[:,i] .= db_array end end #Correlation functions #Higgs if BDIO_get_uinfo(fb) == 12 db_array = similar(Array{Float64, 1}, tim) for i in 1:niter BDIO_read(fb, db_array) h2[1,:,i] .= db_array[1:tim] end for i in 1:niter BDIO_read(fb, db_array) h2[2,:,i] .= db_array[1:tim] end end #W-Boson if BDIO_get_uinfo(fb) == 13 db_array = similar(Array{Float64, 1}, tim) for k in 1:niter for mu in 1:3 for i in 1:3 BDIO_read(fb, db_array) w1[1,mu,i,:,k] .= db_array end end end for k in 1:niter for mu in 1:3 for i in 1:3 BDIO_read(fb, db_array) w1[2,mu,i,:,k] .= db_array end end end for k in 1:niter for mu in 1:3 for i in 1:3 BDIO_read(fb, db_array) w1[3,mu,i,:,k] .= db_array end end end end end return pl, rho1, rho2, Lphi1, Lphi2, Lalp1, Lalp2, dh, flwtime, Echain, Eclchain, h2, w1 end end function generate_run_tag(fname::String, id::Union{Nothing, Int64}=nothing) fb = BDIO_open(fname, "r") nth, niter, flw_s, flw_steps, flw_dtr, flw_iter, beta, flw_dt, k1, et1 = readparams(fb) tag = fname*string(nth)*string(niter)*string(flw_s)*string(flw_steps)*string(flw_dtr)*string(flw_iter)*string(beta)*string(flw_dt)*string(k1)*string(et1) if id != nothing tag = tag*"_id"*string(id) end return tag end function correlator(interpol_chain::Array{T,2}, tag::String, niter_vec::Vector{Int64},wpm=Dict{String,Vector{Float64}}()) where T # interpol_chain: 2x2 array [time, niter] L = length(interpol_chain[:,1]) chainsize = length(interpol_chain[1,:]) #average of interpolator mean = Vector{uwreal}(undef, L) for i in 1:L mean[i] = uwreal( interpol_chain[i,:], tag, niter_vec ) try uwerr(mean[i],wpm) catch e println("uwerr fails computing the mean, take a look at the correlation function - fix window!!") cf = rho(mean[i],tag) p = scatter(cf[1:100]) display(p) return nothing end end #build chain of differences dif_chain = similar(interpol_chain) for k in 1:chainsize dif_chain[:,k] .= interpol_chain[:,k] .- value.(mean) end #correlations ct = Vector{uwreal}(undef, L) for i in 1:L # ct[i] = dif[i]*dif[1] ct[i] = uwreal( dif_chain[i,:].*dif_chain[1,:] , tag, niter_vec) try uwerr(ct[i],wpm) catch e println("uwerr fails computing the differences, take a look at the correlation function - fix window!!") cf = rho(ct[i],tag) p = scatter(cf[1:100]) display(p) end end return ct end function symmass(corr::Vector{uwreal}, d::Int64) L = length(corr) quot = 1/2 * ( corr[d+1] + corr[L-d-1] + corr[d-1] + corr[L-d+1] ) / ( corr[d] + corr[L-d] ) amd = acosh(quot) uwerr(amd) return amd end function effsymmass(corr::Vector{uwreal}) L = length(corr) l2 = convert(Int64, L/2+1) emass = Vector{uwreal}(undef, L-3) #save indices that have a proper value ind = Vector{Int32}() for t in 2:L-2 quot = 1/2 * ( corr[t+1] + corr[L-t-1] + corr[t-1] + corr[L-t+1] ) / ( corr[t] + corr[L-t] ) if value(quot)>1.0 emass[t-1] = acosh(quot) if t<=l2 push!(ind, t-1) end else emass[t-1] = uwreal([0.0,Inf],"") # emass[t-1] = uwreal([0.0,0.0],"") end uwerr(emass[t-1]) end return emass,ind end function massfit(m::Vector{uwreal}, xdata::Vector{Int64}) @. linfunc(x, p) = p[1] + x*0 # xdata = collect(1:length(value.(m))) ydata = value.(m) dydata = err.(m) #lower bound lb = [0.0] #upper bound ub = [Inf] #starting values p0_bounds = [0.0] #fit fit_bounds = curve_fit(linfunc, xdata, ydata, p0_bounds, lower=lb, upper=ub) #chisquared chisq(p, d) = sum( (d .- ( linfunc(xdata,p) ) ) .^ 2 ./ (dydata) .^2 ) #propagate (fitp, csqexp) = fit_error(chisq, coef(fit_bounds), m) chi = chisq(coef(fit_bounds),ydata) mass = fitp[1] uwerr(mass) return mass, chi, dof(fit_bounds) end function fullcorrelator(interpol_chain::Array{T,2}, tag::String) where T #does not remove disconnected component # interpol_chain: 2x2 array [time, niter] L = length(interpol_chain[:,1]) #correlations ct = Vector{uwreal}(undef, L) for i in 1:L # ct[i] = dif[i]*dif[1] ct[i] = uwreal( interpol_chain[i,:].*interpol_chain[1,:] , tag) uwerr(ct[i]) end return ct end function fullcorrelator(interpol_chain1::Array{T,2}, interpol_chain2::Array{T,2}, tag::String) where T #does not remove disconnected component # interpol_chain: 2x2 array [time, niter] L = length(interpol_chain1[:,1]) #correlations ct = Vector{uwreal}(undef, L) for i in 1:L # ct[i] = dif[i]*dif[1] ct[i] = uwreal( interpol_chain1[i,:].*interpol_chain2[1,:] , tag) uwerr(ct[i]) end return ct end """ Linear Extrapolation """ function lin_extrap(xdata::Vector{Float64}, uydata::Vector{uwreal}) @. linfunc(x, p) = p[1]*x + p[2] ydata = value.(uydata) dydata = err.(uydata) #bounds for parameters p[i] #lower bound lb = [-Inf, -Inf] #upper bound ub = [Inf, Inf] #starting values p0 = [0.1, -0.1] #fit fit_bounds = curve_fit(linfunc, xdata, ydata, p0, lower=lb, upper=ub) #chisquared chisq(p, d) = sum( (d .- ( linfunc(xdata,p) ) ) .^ 2 ./ (dydata) .^2 ) #propagate (fitp, csqexp) = fit_error(chisq, coef(fit_bounds), uydata) chi = chisq(coef(fit_bounds),ydata) return fitp, chi, fit_bounds end """ Quadratic fit """ function quad_extrap(xdata::Vector{Float64}, uydata::Vector{uwreal}) @. linfunc(x, p) = p[1]*x^2 + p[2]*x + p[3] ydata = value.(uydata) dydata = err.(uydata) #bounds for parameters p[i] #lower bound lb = [-Inf, -Inf,-Inf] #upper bound ub = [Inf, Inf,Inf] #starting values p0 = [0.1, -0.1,0.1] #fit fit_bounds = curve_fit(linfunc, xdata, ydata, p0, lower=lb, upper=ub) #chisquared chisq(p, d) = sum( (d .- ( linfunc(xdata,p) ) ) .^ 2 ./ (dydata) .^2 ) #propagate (fitp, csqexp) = fit_error(chisq, coef(fit_bounds), uydata) chi = chisq(coef(fit_bounds),ydata) return fitp, chi, fit_bounds end """ Exponential Extrapolation """ function exp_extrap(xdata::Vector{Float64}, uydata::Vector{uwreal}) @. expfunc(x, p) = p[1]*exp(p[2]*x) + p[3] ydata = value.(uydata) dydata = err.(uydata) #bounds for parameters p[i] #lower bound lb = [-Inf, -Inf, -Inf] #upper bound ub = [Inf, Inf, Inf] #starting values p0 = [0.1, -0.1, 0.1] #fit fit_bounds = curve_fit(expfunc, xdata, ydata, p0, lower=lb, upper=ub) #chisquared chisq(p, d) = sum( (d .- ( expfunc(xdata,p) ) ) .^ 2 ./ (dydata) .^2 ) #propagate (fitp, csqexp) = fit_error(chisq, coef(fit_bounds), uydata) chi = chisq(coef(fit_bounds),ydata) return fitp, chi, fit_bounds end """ Plot and calculate root of interpolation with systematic error """ function interpolate_root(c::Float64,xdata::Vector{Float64}, uydata::Vector{uwreal},tag::String,plt::Bool) # npoints closer to t0 totpoints = length(xdata) function find_points(y,np) #not using adaptative step integrator #only need to find the first point closest to c lastn = totpoints for n in totpoints:-1:2 if value(uydata[n]) < y && value(uydata[n-1]) > y lastn = n break end # if n<div(totpoints,2) && value(uydata[n]) > y && value(uydata[n-1]) < y break end end xi = [lastn-i+1 for i in 1:np] return xi end #linear and quadratic p=plot(reuse=false) let t01,t02 for n in 1:2 #find indices to fit ind = find_points(c,n+1) if n == 1 fit1,chi1,bounds1 = lin_extrap(xdata[ind],uydata[ind]) t01 = (c - fit1[2])/fit1[1] uwerr(t01,wpm) if plt print("\nchi^2 / d.o.f.: \n\t", chi1, " / ", dof(bounds1), " = $(chi1/dof(bounds1))\n") print("Distance of interpolation:\t",abs(xdata[ind[1]] - xdata[ind[2]]),"\n") print("Root:\n\t",t01) end f(x)=c g(x) = value(fit1[1])*x+value(fit1[2]) p=plot(xdata[ind],value.(uydata[ind]),yerr=err.(uydata[ind])) p=plot!(f,minimum(xdata[ind]),maximum(xdata[ind])) p=plot!(g,minimum(xdata[ind]),maximum(xdata[ind]),label="Linear") elseif n == 2 fit2,chi2,bounds2 = quad_extrap(xdata[ind],uydata[ind]) if value(fit2[2]^2 - 4.0*fit2[1]*(fit2[3]-c))>0.0 root1 = (-fit2[2]+sqrt(fit2[2]^2 - 4.0*fit2[1]*(fit2[3]-c)))/(2.0*fit2[1]) root2 = (-fit2[2]-sqrt(fit2[2]^2 - 4.0*fit2[1]*(fit2[3]-c)))/(2.0*fit2[1]) else root1 = t01 root2 = t01 print("\n ---- Could not find quadratic root! ----") end if abs(value(root1 - t01)) <= abs(value(root2 - t01)) t02 = root1 else t02 = root2 end p=plot!(xdata[ind],value.(uydata[ind]),yerr=err.(uydata[ind])) h(x) = value(fit2[1])*x^2+value(fit2[2])*x+value(fit2[3]) p=plot!(h,minimum(xdata[ind]),maximum(xdata[ind]),label="Quadratic") uwerr(t02,wpm) end end sys = value(t01-t02) if plt display(p) print(" [",sys,"]\n") else print("\nRoot for $c:\n\t",t01) print(" [",sys,"]\n") end return t01,sys end end """ Plot and calculate root of interpolation with systematic error - same as the SCALING project """ function interpolate_root_s(c::Float64,xdata::Vector{Float64}, uydata::Vector{uwreal},tag::String,plt::Bool) # npoints closer to t0 totpoints = length(xdata) function find_points(y,np) xi = Vector{Int64}(undef,np) #store indices for the points mindif = 0.0 xindex = 1 for i in 1:np maxdif = Inf ndif = 0.0 for n in 1:totpoints ndif = abs(value(uydata[n]) - y) if ndif > mindif && ndif < maxdif xindex = n xi[i] = n maxdif = ndif end end mindif = abs(value(uydata[xi[i]]) - y) end return xi end #linear and quadratic p=plot(reuse=false) let t01,t02 for n in 1:2 #find indices to fit ind = find_points(c,n+1) if n == 1 fit1,chi1,bounds1 = lin_extrap(xdata[ind],uydata[ind]) t01 = (c - fit1[2])/fit1[1] uwerr(t01) if plt print("\nchi^2 / d.o.f.: \n\t", chi1, " / ", dof(bounds1), " = $(chi1/dof(bounds1))\n") print("Distance of interpolation:\t",abs(xdata[ind[1]] - xdata[ind[2]]),"\n") print("Root:\n\t",t01) end f(x)=c g(x) = value(fit1[1])*x+value(fit1[2]) p=plot(xdata[ind],value.(uydata[ind]),yerr=err.(uydata[ind])) p=plot!(f,minimum(xdata[ind]),maximum(xdata[ind])) p=plot!(g,minimum(xdata[ind]),maximum(xdata[ind]),label="Linear") elseif n == 2 fit2,chi2,bounds2 = quad_extrap(xdata[ind],uydata[ind]) if value(fit2[2]^2 - 4.0*fit2[1]*(fit2[3]-c))>0.0 root1 = (-fit2[2]+sqrt(fit2[2]^2 - 4.0*fit2[1]*(fit2[3]-c)))/(2.0*fit2[1]) root2 = (-fit2[2]-sqrt(fit2[2]^2 - 4.0*fit2[1]*(fit2[3]-c)))/(2.0*fit2[1]) else root1 = t01 root2 = t01 print("\n ---- Could not find quadratic root! ----") end if abs(value(root1 - t01)) <= abs(value(root2 - t01)) t02 = root1 else t02 = root2 end p=plot!(xdata[ind],value.(uydata[ind]),yerr=err.(uydata[ind])) h(x) = value(fit2[1])*x^2+value(fit2[2])*x+value(fit2[3]) p=plot!(h,minimum(xdata[ind]),maximum(xdata[ind]),label="Quadratic") uwerr(t02) end end sys = value(t01-t02) if plt display(p) print(" [",sys,"]\n") else print("\nRoot for $c:\n\t",t01) print(" [",sys,"]\n") end return t01,sys end end mutable struct h2sim{T} #specific struct for 2HDM simulations gp::GaugeParm{T,SU2{T},0} sp::ScalarParm{2,T} mH::Vector{uwreal} #mH1, mH2 mW::Vector{uwreal} #mW1, mW2, mW12 t0::Vector{uwreal} #t0, t1, .. w0::Vector{uwreal} flwtime::Vector{Float64} tEpl::Vector{uwreal} tEcl::Vector{uwreal} glb::Vector{uwreal} #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 dim::NTuple{2,Int64} #space, time sm::smr{T} #smearing end mutable struct h2runs{T} #specific struct for 2HDM simulations gp::GaugeParm{T,SU2{T},0} sp::ScalarParm{2,T} mH::Vector{uwreal} #mH1, mH2 mW::Vector{uwreal} #mW1, mW2, mW12 t0::Vector{uwreal} #t0, t1, .. w0::Vector{uwreal} flwtime::Vector{Float64} tEpl::Vector{uwreal} tEcl::Vector{uwreal} glb::Vector{uwreal} #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 end """ Save masses and gradient flow measurements for various couplings """ function write_cruns_bdio(fname::String, runs::Vector{h2sim}) fb = BDIO_open(fname, "d", string("")) for i in 1:length(runs) r = runs[i] sp = r.sp beta = (r.gp).beta BDIO_start_record!(fb, BDIO_BIN_F64LE, 2) BDIO_write!(fb,[beta, sp.kap[1], sp.kap[2], sp.eta[1], sp.eta[2], sp.muh, sp.xi[1], sp.xi[2], sp.xi[3], sp.xi[4], r.sm.dt, r.sm.r]) BDIO_start_record!(fb, BDIO_BIN_F64LE, 3) BDIO_write!(fb,[r.dim[1], r.dim[2], r.sm.sus, r.sm.n]) #mH for i in 1:2 write_uwreal(r.mH[i],fb,4) end #mW for i in 1:3 write_uwreal(r.mW[i],fb,5) end #t0, w0 for i in 1:length(r.t0) try write_uwreal(r.t0[i],fb,6) catch e @warn string("No t0 to save.") end end for i in 1:length(r.w0) try write_uwreal(r.w0[i],fb,8) catch e @warn string("No w0 to save.") end end #flow times BDIO_start_record!(fb, BDIO_BIN_F64LE, 9) BDIO_write!(fb,r.flwtime) #write all flow measurements len = length(r.flwtime) try for i in 1:length(value.(r.tEpl)) write_uwreal(r.tEpl[i],fb,10) end catch e @warn string("Plaquette not written") end try for i in 1:length(value.(r.tEcl)) write_uwreal(r.tEcl[i],fb,11) end catch e @warn string("Clover not written") end for i in 1:7 write_uwreal(r.glb[i],fb,12) end end BDIO_close!(fb) end function read_cruns_bdio(fname::String) #add to vector oldruns = Vector{h2sim}() fb = BDIO_open(fname, "r") let flen, fin, glb_count cplgs = Vector{Float64}(undef, 12) #10= nr couplings for 2HDM, 2 smearing parameters ints = Vector{Float64}(undef, 4) mHvec = Vector{uwreal}() mWvec = Vector{uwreal}() t0vec = Vector{uwreal}() w0vec = Vector{uwreal}() flwtime = Vector{Float64}() tEpl = Vector{uwreal}() tEcl = Vector{uwreal}() glbvec = Vector{uwreal}() glb_count = 0 while BDIO_seek!(fb) sav = false if BDIO_get_uinfo(fb) == 2 mHvec = Vector{uwreal}() mWvec = Vector{uwreal}() t0vec = Vector{uwreal}() w0vec = Vector{uwreal}() flwtime = Vector{Float64}() tEpl = Vector{uwreal}() tEcl = Vector{uwreal}() cplgs = Vector{Float64}(undef, 12) BDIO_read(fb, cplgs) nmH = 0 end if BDIO_get_uinfo(fb) == 3 ints = Vector{Int64}(undef, 4) BDIO_read(fb, ints) end if BDIO_get_uinfo(fb) == 4 mH=read_uwreal(fb) push!(mHvec,mH) end if BDIO_get_uinfo(fb) == 5 mW=read_uwreal(fb) push!(mWvec,mW) end if BDIO_get_uinfo(fb) == 6 r = true while r try t0=read_uwreal(fb) push!(t0vec,t0) catch e r = false end end end if BDIO_get_uinfo(fb) == 8 r = true while r try w0=read_uwreal(fb) push!(w0vec,w0) catch e r = false end end end if BDIO_get_uinfo(fb) == 9 flen = round(Int64, BDIO_get_len(fb)/8) f = Vector{Float64}(undef, flen) try BDIO_read(fb, f) push!(flwtime,f) catch e @warn string("No flowtime") end end if BDIO_get_uinfo(fb) == 10 try e=read_uwreal(fb) push!(tEpl,e) catch e @warn string("No flowtime") end if length(tEpl) == flen push!(tEplvec, tEpl) end end if BDIO_get_uinfo(fb) == 11 try e=read_uwreal(fb) push!(tEcl,e) catch e @warn string("No flowtime") end if length(tEcl) == flen push!(tEclvec, tEcl) end end if BDIO_get_uinfo(fb) == 12 glb_count += 1 glb=read_uwreal(fb) push!(glbvec,glb) end #print after measuring the last uinfo - 7 global observables if glb_count == 7 sav = true glb_count = 0 end if sav gp = GaugeParm{Float64}(SU2{Float64}, cplgs[1],1.0) sp = ScalarParm((cplgs[2],cplgs[3]), (cplgs[4],cplgs[5]), cplgs[6], (cplgs[7],cplgs[8],cplgs[9],cplgs[10])) dm = (ints[1],ints[2]) smear = smr{Float64}(ints[3], cplgs[11], ints[4], cplgs[12]) sim = h2sim(gp, sp, mHvec, mWvec, t0vec, w0vec, flwtime, tEpl, tEcl,glbvec,dm,smear) push!(oldruns, sim) glbvec = Vector{uwreal}() end end end BDIO_close!(fb) return oldruns end """ Model average - compute effective mass """ ## mpi is a uwreal vector containing your effective mass as a function of the euclidean time ## tmin = [1,2,3,4,5,10,15] ## tmax = [20] ## @.model(x,p) = p[1] + p[2] * exp(-p[3] * x) + ... ## you are interested in p[1], which is the ground state signal of the effective mass (or any other observable actually), i.e. it is what you would extract from a plateau ## k = 3 ## k has to be the number of parameters of "model" ## meff_average, systematic_error = bayesian_av(model, mpi, tmin, tmax, k, plot_bayesian=true) ## meff_average will have inside the statistichal error handled by ADerrors, and you must add the systematic uncertainty coming from the fit range variation, which is stored in systematic_error. You can do that e.g. by p1_mean + uwreal([0.0,value(systematic_error)], "systematic uncertainty") function fit_defs(f::Function,x,W) ## uncorrelated fit chisq(p,d) = sum((d .- f(x,p)).^2 .* W) return chisq end function bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k::Int64; plot_bayesian::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing) weight_model = Array{Float64,1}() ## here you save the weight of each fit AIC = Array{Float64,1}() ## information criterium to get the weight of each fit chi2chi2exp = Array{Float64,1}() ## here you will save chi2/chi2_exp p1 = Array{uwreal,1}() ## here you will save the result of p[1] for each fit mods = Array{String,1}() ## a label for the fit range used in each fit total = length(y) ## total number of points in the effective mass isnothing(wpm) ? uwerr.(y) : for i in 1:length(y) uwerr(y[i],wpm) end for INDEX in tmin_array ## vary tmin for j in tmax_array ## vary tmax, in your case you may want to leave tmax fixed, so you use tmax_array = [a] with a some fixed value try x = [i for i in INDEX+1:1:j] yy = y[INDEX+1:1:j] #remove points where meff is ill defined for i in 1:length(yy) if (value(yy[i]) == 0.0 && err(yy[i]) == 0.0) deleteat!(yy, i) deleteat!(x, i) end end Ncut = total - length(x) ## number of points not included in the fit dy = err.(yy) W = 1 ./ dy .^2 ## uncorrelated fit so you use the diagonal part of the inverse covariance matrix as weight for the fits p00 = [0.5 for i in 1:1:k] ## initial guess for the fit parameters chisq = fit_defs(fun,x,W) fit = curve_fit(fun,x,value.(yy),W,p00) isnothing(wpm) ? (up,chi_exp) = fit_error(chisq,coef(fit),yy) : (up,chi_exp) = fit_error(chisq,coef(fit),yy,wpm) isnothing(wpm) ? uwerr(up[1]) : uwerr(up[1],wpm) chi2 = sum(fit.resid.^2) * dof(fit) / chi_exp ## or chi2 = sum(fit.resid.^2) if isnan(chi2) continue end push!(AIC, chi2 + 2*k + 2*Ncut) ## compute the info criterium of this individual fit push!(chi2chi2exp, chi2 / dof(fit)) push!(p1, up[1]) ## save the result of your fit parameter of interest for each individual fit (you can modify this to save all fit parameters actually) push!(mods,string("[", INDEX+1, ",", j, "]")) ## label to identify later the different fit ranges you explored catch e # @warn string(":/ error propagation ill at tmin = ", INDEX, ", tmax = ", j) ## sometimes for tmin very close to the boundary/source of your lattice, uwerr() fails (maybe including more exponentials in the fit it will not) end end end offset = minimum(AIC) AIC = AIC .- offset # print(AIC) weight_model = exp.(-0.5 .* AIC) p1_mean = sum(p1 .* weight_model)/sum(weight_model) isnothing(wpm) ? uwerr(p1_mean) : uwerr(p1_mean,wpm) weight_model = weight_model ./ sum(weight_model) ## normalize weights systematic_err = sqrt(sum(p1 .^ 2 .* weight_model) - (sum(p1 .* weight_model)) ^ 2) #; uwerr(systematic_err) ## systematic error coming from the fit range variation, you must add this to p1_mean, which will have the statistical error handled by ADerrors if plot_bayesian == true x = 1:length(p1) yv = value.(p1) dyv = err.(p1) v = value(p1_mean) e = err(p1_mean) #plot value with correlation function p=plot(reuse=false) mf(x) = v dmf(x) = e ylabel = L"$m_{eff}(t)$" xlabel = L"t" l2 = maximum(tmax_array) p=plot(value.(y[begin:l2]),yerr=err.(y[begin:l2]),label=L"m_{eff}") p=plot!(mf,ribbon=dmf,1,l2,label="",xlabel=xlbl,ylabel=ylbl,legend=:bottomright) display(p) #plot average for each range q=plot(value.(p1),yerror=err.(p1),xticks=([i for i in 1:length(p1)], [mods[i] for i in 1:length(p1)]),ylabel=L"p_1",label="Model result") q=plot!(mf,ribbon=dmf,1,length(p1),label="Range average") display(q) k=plot(reuse=false) k=plot!(weight_model,xticks=([i for i in 1:length(p1)], [mods[i] for i in 1:length(p1)]),ylabel=L"p_1",label="Weight") display(k) # fig = figure(figsize = (14.0,6.0)) # fill_between(1:length(p1), v-e, v+e, color="green", alpha=0.75) # errorbar(mods, yv, dyv, fmt="x", color="black") # ylabel(L"$p_1$") # xlabel(L"model") # # xticks(rotation=90) # fig = figure(figsize = (14.0,6.0)) # errorbar(mods, weight_model, 0*dyv, color="green") # ylabel(L"$weight$") # xlabel(L"model") # # xticks(rotation=90) end return (p1_mean, systematic_err) end #TODO REDEFINIR ISTO function obs(n::h2runs, L::Int64) a = sqrt(8*n.t0)*n.mH uwerr(a,wpm) b = sqrt(8*n.t0)*n.mW uwerr(b,wpm) c = n.mH/n.mW uwerr(c,wpm) uwerr(n.mH,wpm) uwerr(n.mW,wpm) d = n.w0/n.t0 uwerr(d,wpm) uwerr(n.t0,wpm) uwerr(n.w0,wpm) f = n.mW*L uwerr(f) gg = sqrt(8*n.t0)/L uwerr(gg) return n.bt,n.k,n.l,a,b,c,n.mH,n.mW,d,n.t0,n.w0,f,gg end """ Time derivative """ function timederiv(xdata::Vector,ydata::Vector{uwreal},dx, wpm) dydx = Vector{uwreal}() Ct = Vector{uwreal}() times = Vector{Float64}() for i in 3:length(xdata)-1 dif = maximum([abs(xdata[i+1]-xdata[i]);abs(xdata[i-1]-xdata[i])]) if dif > 2*dx continue else t = xdata[i] push!(times,t) W = t*( ydata[i+1] - ydata[i-1] )/(2.0*dx) uwerr(W, wpm) push!(dydx,W) C = ydata[i]^2/W uwerr(C,wpm) push!(Ct,C) end end return times, dydx, Ct end #weighted average function w_ave(a::uwreal,b::uwreal) wa = 1/err(a)^2 wb = 1/err(b)^2 ave = (wa*a + wb*b)/(wa+wb) uwerr(ave) return ave end """ Higgs masses """ function get_higgs(h::Array{T,2}, tag, niter_vec, cs, ce) where {T<:AbstractFloat} #h : interpolator [time; Markov chain] #niter_vec : replica vector #cs : cut start time for the fit #ce : cut end time for the fit print("getting correlator.\n") #Average Correlator Chiggs1 = correlator(h[:,:], tag, niter_vec) for t in 1:length(h2[1,:,1]) Chiggs1[t] *= 1/(Chiggs1[end]) uwerr(Chiggs1[t]) end print("Done.\n") print("getting effective mass.\n") #Effective Mass emass = effsymmass(Chiggs1) print("Done.\n") return emass # #Model average - Fit constant # @.funn(x,p) = p[1] + x*0.0 # l2 = convert(Int64, length(h)/2)+1 # tstart = collect(ce:l2-cs) # tend = collect(ce:l2) # print("getting mass.\n") # (mH,sysH) = bayesian_av(funn, emass, tstart, tend, 1, plot_bayesian=false) # print("Done.\n") # mH = mH + uwreal([0.0,value(sysH)], "systematic uncertainty") # uwerr(mH) # print("Model average - Effective Mass:\n",mH,"\n",sysH) # return mH # return nothing end """ W-boson mass """ function get_w(w::Array{T,4}, tag, niter_vec, cs, ce) where {T<:AbstractFloat} #w : w-boson interpolator [ Link direction; Colour index; time; Markov chain] CW = Array{uwreal,3}(undef,3, 3, tim) for mu in 1:3 for i in 1:3 #correlator for each Direction and Colour CW[mu,i,:] = correlator(w[1,mu,i,:,:], tag, niter_vec) for t in 1:tim CW[mu,i,t] *= 1/(CW[mu,i,end]) uwerr(CW[mu,i,t]) end end end #average over mu and i CWmean = Vector{uwreal}(undef, tim) for t in 1:tim CWmean[t] = sum(CW[:,:,t])/(3*3) uwerr(CWmean[t]) end Wemass = effsymmass(CWmean) L = length(value.(CWmean[1,:])) l2 = convert(Int64, L/2)+1 @.funn(x,p) = p[1] + x*0.0 tstart = collect(4:l2-cs) tend = collect(ce:l2) (mW,sysW) = bayesian_av(funn, Wemass, tstart, tend, 1, plot_bayesian=true) mW = mW + uwreal([0.0,value(sysH)], "systematic uncertainty") uwerr(mW) print("Model average - Effective Mass:\n",mW,"\n",sysW) return mW end #+end_src #+RESULTS: ** Functions - full GF *** TODO THIS WAS NOT UPDATED FROM THE 1-HIGGS CODE! #+begin_src jupyter-julia :session h2ps function readparams(fb) #move read position to the first record BDIO_seek!(fb,0) let nth, niter, flw_s, flw_steps, flw_dtr, flw_iter, beta, flw_dt, k1,et1 while BDIO_seek!(fb) # Simulation parameters if BDIO_get_uinfo(fb) == 1 int_array = similar(Array{Int64, 1}, 6) BDIO_read(fb, int_array) nth = int_array[1] niter = int_array[2] flw_s = int_array[3] flw_steps = int_array[4] flw_dtr = int_array[5] flw_iter = int_array[6] end if BDIO_get_uinfo(fb) == 2 flt_array = similar(Array{Float64, 1}, 4) BDIO_read(fb, flt_array) beta = flt_array[1] flw_dt = flt_array[2] k1 = flt_array[3] et1 = flt_array[4] end end return nth, niter, flw_s, flw_steps, flw_dtr, flw_iter, beta, flw_dt, k1, et1 end end function readdata(fb,nth, niter, flw_iter, flw_steps) #move read position to the first record k_index = 0 BDIO_seek!(fb,0) let pl, rho1, rho2, Lphi1, Lphi2, Lalp1, Lalp2, dh, flwtime, Echain, Eclchain, h2, w1r, w1i flwtime = Vector{Float64}(undef, flw_steps) Echain = Array{Float64,2}(undef, flw_iter, flw_steps) Eclchain = Array{Float64,2}(undef, flw_iter, flw_steps) h2 = Array{Float64,3}(undef, 1, tim, niter) w1 = Array{Float64,5}(undef, 1, 3, 3, tim, niter) while BDIO_seek!(fb) #observables #Plaquette if BDIO_get_uinfo(fb) == 3 k_index += 1 db_array = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array) pl = db_array end # Rho if BDIO_get_uinfo(fb) == 4 db_array = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array) rho1 = db_array end # Lphi if BDIO_get_uinfo(fb) == 5 db_array = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array) Lphi1 = db_array end # Lalp if BDIO_get_uinfo(fb) == 6 db_array = similar(Array{Float64, 1}, nth+niter) BDIO_read(fb, db_array) Lalp1 = db_array end #dh if BDIO_get_uinfo(fb) == 8 db_array = similar(Array{Float64, 1}, nth) BDIO_read(fb, db_array) dh = db_array end # FLOW # Flow time if BDIO_get_uinfo(fb) == 9 db_array = similar(Array{Float64, 1}, flw_steps) BDIO_read(fb, db_array) flwtime = db_array end #Flow E if BDIO_get_uinfo(fb) == 10 db_array = similar(Array{Float64, 1}, flw_iter) for i in 1:flw_steps BDIO_read(fb, db_array) Echain[:,i] .= db_array end end #Flow E - Clover if BDIO_get_uinfo(fb) == 11 db_array = similar(Array{Float64, 1}, flw_iter) for i in 1:flw_steps BDIO_read(fb, db_array) Eclchain[:,i] .= db_array end end #Correlation functions #Higgs if BDIO_get_uinfo(fb) == 12 db_array = similar(Array{Float64, 1}, tim) for i in 1:niter BDIO_read(fb, db_array) h2[1,:,i] .= db_array[1:tim] end end #W-Boson if BDIO_get_uinfo(fb) == 13 db_array = similar(Array{Float64, 1}, tim) for k in 1:niter for mu in 1:3 for i in 1:3 BDIO_read(fb, db_array) w1[1,mu,i,:,k] .= db_array end end end end end return pl, rho1, Lphi1, Lalp1, dh, flwtime, Echain, Eclchain, h2, w1 end end function generate_run_tag(fname::String, id::Union{Nothing, Int64}=nothing) fb = BDIO_open(fname, "r") nth, niter, flw_s, flw_steps, flw_dtr, flw_iter, beta, flw_dt, k1, et1 = readparams(fb) tag = fname*string(nth)*string(niter)*string(flw_s)*string(flw_steps)*string(flw_dtr)*string(flw_iter)*string(beta)*string(flw_dt)*string(k1)*string(et1) if id != nothing tag = tag*"_id"*string(id) end return tag end #+end_src #+RESULTS: * Main code ** Read File; Global Arrays #+begin_src jupyter-julia :session h2ps global lat=24 global tim=24 global dims=(lat,tim) files = [ "h2_2_24x24x24x24_beta6.0_k10.133_k20.1288_etas0.003_0.001_mu0.001_xis0.0001_0.0001_0.0005_0.0001_niter12000_eps0.05_nsteps35_SM_us_20_0.01_ss_40_0.02.bdio" ] tag = "" sdims="$(lat)x$(lat)x$(lat)x$(tim)" if !occursin(sdims, files[1]) throw("Check Lattice Dimensions") end nr_rep = length(files) niter_vec = Vector{Int64}(undef,nr_rep) global nth,niter,flw_steps,flw_dtr,flw_iter,sus,sss=0,0,0,0,0,0,0 global gp, sp global flw_s = 0 global flw_dt,sdt,srs = 0.0,0.0,0.0 #MC chain length for GF tot_flw_iter = 0 for i in 1:nr_rep filename = "simulations/"*files[i] print("\n",filename,"\n\n") tag *= generate_run_tag(filename) fb = BDIO_open(filename, "r") nth, niter, flw_steps, flw_dtr, flw_iter, sus, sss, flw_dt, gp, sp, sdt, srs = readparams(fb) niter_vec[i] = niter tot_flw_iter += flw_iter print(sp) @printf("\n ## GLOBAL:\n\t Niter: %d \n\t Ntherm: %d ", niter, nth) @printf("\n\n ## FLOW:\n\t Flow Observations: %d \n\t Flow Trajectory: %d \n\t Flow Step-size: %1.3f \n\t Time between measurements: %.3f \n", flw_iter,flw_steps,flw_dt, flw_dt) BDIO_close!(fb) end smear = smr{Float64}(sus,sdt,sss,srs) tot_conf = sum(niter_vec) global pl = Vector{Float64}(undef, tot_conf) global rho1 = Vector{Float64}(undef, tot_conf) global rho2 = Vector{Float64}(undef, tot_conf) global Lphi1 = Vector{Float64}(undef, tot_conf) global Lphi2 = Vector{Float64}(undef, tot_conf) global Lalp1 = Vector{Float64}(undef, tot_conf) global Lalp2 = Vector{Float64}(undef, tot_conf) global dh = Vector{Float64}(undef, tot_conf) #corr global h2 = Array{Float64,3}(undef,2, tim, tot_conf) global w1 = Array{Float64,5}(undef,3, 3, 3, tim, tot_conf) # flow global flwtime = Vector{Float64}(undef, flw_steps) global Echain = Array{Float64, 2}(undef, tot_flw_iter, flw_steps) global dEchain = Array{Float64, 2}(undef, tot_flw_iter, flw_steps) global Eclchain = Array{Float64, 2}(undef, tot_flw_iter, flw_steps) global dEclchain = Array{Float64, 2}(undef, tot_flw_iter, flw_steps) nconf = 0 GF_iter = 0 for i in 1:nr_rep filename = "simulations/"*files[i] fb = BDIO_open(filename, "r") nth, niter, flw_steps, flw_dtr, flw_iter, sus, sss, flw_dt, gp, sp, sdt, srs = readparams(fb) #data rpl, rrho1, rrho2, rLphi1, rLphi2, rLalp1, rLalp2, rdh, flwtime, rEchain, rEclchain, rh2, rw1 = readdata(fb, nth, niter, flw_iter, flw_steps) beg = nconf+1 fin = nconf+niter pl[beg:fin] = rpl[nth+1:end] rho1[beg:fin] = rrho1[nth+1:end] rho2[beg:fin] = rrho2[nth+1:end] Lphi1[beg:fin] = rLphi1[nth+1:end] Lphi2[beg:fin] = rLphi2[nth+1:end] Lalp1[beg:fin] = rLalp1[nth+1:end] Lalp2[beg:fin] = rLalp2[nth+1:end] dh[beg:fin] = rdh[nth+1:end] Echain[GF_iter+1:GF_iter+flw_iter,:] = rEchain Eclchain[GF_iter+1:GF_iter+flw_iter,:] = rEclchain h2[:,:,beg:fin] = rh2 w1[:,:,:,:,beg:fin] = rw1 nconf += niter GF_iter += flw_iter BDIO_close!(fb) end #TODO MUDAR ISTO A CADA RUN thm = 100 tag = tag*string(thm) #+end_src #+RESULTS: #+begin_example simulations/h2_2_24x24x24x24_beta6.0_k10.133_k20.1288_etas0.003_0.001_mu0.001_xis0.0001_0.0001_0.0005_0.0001_niter12000_eps0.05_nsteps35_SM_us_20_0.01_ss_40_0.02.bdio - etas: - mu12: - xi: Number of scalar fields: 2 - Kappas: 0.133 0.1288 - etas: 0.003 0.001 - mu12: 0.001 - xi: (0.0001, 0.0001, 0.0005, 0.0001) ## GLOBAL: Niter: 12000 Ntherm: 1000 ## FLOW: Flow Observations: 1200 Flow Trajectory: 25 Flow Step-size: 0.005 Time between measurements: 0.005 #+end_example ** Histories *** Energy Conservation #+begin_src jupyter-julia :session h2ps plot(reuse=false) p=plot(dh[begin:10:end]) display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/9e0a96f6fbf1faac0e1a85db9efd9334f54b2aa3.svg]] *** Global **** $P$ #+begin_src jupyter-julia :session h2ps s=1 c=niter p=plot(pl[s:10:end]) display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/91d4afd27efad26b41dac1b49dd08ec393af9007.svg]] **** $\rho$ #+begin_src jupyter-julia :session h2ps s=1 c=10 p=plot(rho2[s:10:c],label=L"\rho_1") display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/4b760d940ec2f8a49d4f4c17f356181fcb05ea1b.svg]] **** $L_\phi$ #+begin_src jupyter-julia :session h2ps s=1 c=niter p=plot(Lphi1[s:1:c],label=L"{L_\phi}_1") p=plot!(Lphi2[s:1:c],label=L"{L_\phi}_1") display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/f4641690eb483df60a121554c1145046dd3ff2dd.svg]] **** $L_\alpha$ #+begin_src jupyter-julia :session h2ps s=1 p=plot(Lalp1[s:10:end],label=L"{L_\alpha}_1") p=plot!(Lalp2[s:10:end],label=L"{L_\alpha}_1") # print(Lalp1[100:200]) display(p) # outputname = "/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/mcchain_Lalp_beta$(beta)_kappa$(k1)_l$(eta).png" # print(outputname) # savefig(outputname) #+end_src #+RESULTS: [[file:./.ob-jupyter/5d01bfaf3f730f4ddabddd146e66a3e7809941bf.svg]] *** Flow **** Plaquette ***** E(t) #+begin_src jupyter-julia :session h2ps plot(reuse=false) t=50 it=1 iT=1000 p = plot!(Echain[begin:end,t]) display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/21d7f07161aa229d2a7009c6afcd649ace3b632c.svg]] ***** dE(t)/dt #+begin_src jupyter-julia :session h2ps plot(reuse=false) # p = scatter!(dEchain[1,:]) s=1 c=flw_steps for i in 2:flw_iter p = scatter!(dEchain[i,s:c],legend=false) end display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/afe6958be5d73416b6574df3177788844bfcba34.svg]] ***** Numerical derivative #+begin_src jupyter-julia :session h2ps nrdv = [] iter = 10 for i in 2:flw_steps dedt = (Echain[iter,i] - Echain[iter,i-1])/(flw_dt*flw_s) push!(nrdv, dedt) end s=15 c=flw_steps-1 q = scatter(flwtime[s+1:c].-(flw_dt*flw_s/2.0), nrdv[s:c],label="Numerical - midpoint") q = scatter!(flwtime[s:c],dEchain[iter,s:c],label="Explicit",grid=:show,gridstyle=:dot,legend=:bottomright) display(q) #+end_src #+RESULTS: [[file:./.ob-jupyter/67332cfb3f0d192894d3967e6acb90d1cb0118c5.svg]] **** Clover ***** E(t) #+begin_src jupyter-julia :session h2ps plot(reuse=false) t=40 it=1 iT= p = plot!(Eclchain[it:end,t]) display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/bae781b13eec35185466dbf7686770d087238fe5.svg]] [[file:./.ob-jupyter/181ca19d7c0869ebc5721926daeef8c66064dd9f.svg]] ***** dE(t)/dt #+begin_src jupyter-julia :session h2ps plot(reuse=false) # p = scatter!(dEchain[1,:]) for i in 2:niter p = scatter!(dEclchain[i,:]) end display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/12f27276cdaa663d754e30f88fad89cfae666290.svg]] [[file:./ ***** Numerical derivative #+begin_src jupyter-julia :session h2ps nrdv = [] iter = 10 for i in 2:flw_steps dedt = -0.05*(Eclchain[iter,i] - Eclchain[iter,i-1])/(flw_dt*flw_s) push!(nrdv, dedt) end s=1 cut=flw_steps-1 q = scatter(flwtime[s+1:cut].-(flw_dt*flw_s/2.0), nrdv[s:cut],label="Numerical - midpoint") q = scatter!(flwtime[s:cut],dEclchain[iter,s:cut],label="Explicit",grid=:show,gridstyle=:dot) display(q) #+end_src #+RESULTS: [[file:./.ob-jupyter/2110dccb301890c2f5076cb6e324ca8e41255096.svg]] **** Clover VS Plaquette #+begin_src jupyter-julia :session h2ps plot(reuse=false) iter=20 p = scatter(flwtime,flwtime.^2 .*Echain[iter,:],label="Plaq.") p = scatter!(flwtime,flwtime.^2 .*Eclchain[iter,:],label="Clover") display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/90ac57492656d3ae480f29551cf83c6d24d9c614.svg]] *** Correlators **** Higgs #+begin_src jupyter-julia :session h2ps t=3 ttl = latexstring("t=$(t),~\\kappa_1 = \\kappa_2 = $(sp.kap[1])") ylbl = latexstring("H(t)") s = 1 p = plot(h2[1,t,s:1:end],label=L"H_1",title=ttl,ylabel=ylbl, xlabel="#",legend=:topleft) p = plot!(h2[2,t,s:1:end],label=L"H_2") display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/therm_h1_h2_k1_$(sp.kap[1])_k2_$(sp.kap[2])_lat$(lat)x$(tim)_b$(bet).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/173816933808c2850ee88a290f5cf4ba8b9e5fed.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/therm_h1_h2_k1_0.133_k2_0.1292_lat24x24_b5.5.png :END: **** W-Boson #+begin_src jupyter-julia :session h2ps t=1 ttl = latexstring("t=$(t),~\\kappa_1 = \\kappa_2 = $(sp.kap[1])") ylbl = latexstring("W_{\\mu,i}(t)") p = plot(w1[1,1,3,t,begin:1:end],label=L"W_1",title=ttl,ylabel=ylbl, xlabel="#") p = plot!(w1[2,1,3,t,begin:1:end],label=L"W_2") # p = plot!(w1[3,1,3,t,begin:1:end],label=L"W_2") display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/therm_w1_w2_k1_$(sp.kap[1])_k2_$(sp.kap[2])_lat$(lat)x$(tim)_b$(bet).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/ba0f7d1b69f94be1007eb46f03a5f0132acf9247.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/therm_w1_w2_k1_0.133_k2_0.1287_lat24x24_b5.5.png :END: ** Averages *** Global Observables #+begin_src jupyter-julia :session h2ps plmean = uwreal(pl[thm:end],tag,niter_vec .- (thm-1)) rho1mean = uwreal(rho1[thm:end],tag,niter_vec .- (thm-1)) rho2mean = uwreal(rho2[thm:end],tag,niter_vec .- (thm-1)) Lphi1mean = uwreal(Lphi1[thm:end],tag, niter_vec .- (thm-1)) Lphi2mean = uwreal(Lphi2[thm:end],tag, niter_vec .- (thm-1)) Lalp1mean = uwreal(Lalp1[thm:end],tag,niter_vec .- (thm-1)) Lalp2mean = uwreal(Lalp2[thm:end],tag,niter_vec .- (thm-1)) glbvec = [plmean, rho1mean, rho2mean, Lphi1mean, Lphi2mean, Lalp1mean, Lalp2mean] uwerr.(glbvec) print.(glbvec,"\n") #+end_src #+RESULTS: : 1.7495596100891508 +/- 7.494998100001513e-6 : 5.90671202089584 +/- 0.0013810784044758035 : 3.5599691364105492 +/- 0.0016502924069776563 : 3.873923347555751 +/- 0.0013592035175061436 : 1.5339459251948406 +/- 0.0016064107563622915 : 1.7849646935623054 +/- 0.00020126825718718049 : 1.1567202545031519 +/- 0.0007148555800680899 **** Plaquette #+begin_src jupyter-julia :session h2ps plmean = uwreal(pl[begin:end],tag,niter_vec) uwerr(plmean) print("Observable <P> = < Tr Up>\n\t - Value:\t", plmean ) #+end_src #+RESULTS: : Observable <P> = < Tr Up> : - Value: 1.7403978232882378 +/- 6.26358158551182e-5 **** $\rho^2$ #+begin_src jupyter-julia :session h2ps rhomean = uwreal(rho1[begin:end],tag,niter_vec) uwerr(rhomean) print("Observable <r>\n\t - Value:\t", rhomean ) #+end_src #+RESULTS: : Observable <r> : - Value: 2.0420459089918257 +/- 0.001454007694729225 **** $L_\phi$ #+begin_src jupyter-julia :session h2ps Lphimean = uwreal(Lphi1[begin:end],tag, niter_vec) uwerr(Lphimean) print("Observable <R>\n\t - Value:\t", Lphimean ) #+end_src #+RESULTS: : Observable <R> : - Value: 0.37574020158664523 +/- 0.00162917447528644 **** $L_\alpha$ #+begin_src jupyter-julia :session h2ps Lalpmean = uwreal(Lalp1[begin:end],tag,niter_vec) uwerr(Lalpmean) print("Observable <L>\n\t - Value:\t", Lalpmean ) #+end_src #+RESULTS: : Observable <L> : - Value: 0.6719116569850095 +/- 0.0005483092687432931 *** Flow Average $\dv{t^2 <E(t)>}{t} = 2t<E(t)> + t^2\dv{<E(t)>}{t}$ #+begin_src jupyter-julia :session h2ps global E = Vector{uwreal}(undef, flw_steps) global Ecl = Vector{uwreal}(undef, flw_steps) therm = 0 wpm = Dict{String,Vector{Float64}}() uncorr_id = string("noncorrelated_",therm,flw_iter) wpm[uncorr_id] = [1.0, -1.0, -1.0, -1.0] # ID = string("therm",therm,c,filename,flw_iter) ID = tag # ID = uncorr_id for t in 1:flw_steps # E[t] = uwreal( flwtime[t]^2*Echain[(therm+1):c,t], ID ) # uwerr(E[t],wpm) Ecl[t] = uwreal(flwtime[t].^2 .*Eclchain[begin:end,t], ID, niter_vec, collect(flw_dtr:flw_dtr:sum(niter_vec)), sum(niter_vec)) uwerr(Ecl[t],wpm) # print(taui(Ecl[t],ID),"\n") end global dEdt = Vector{uwreal}(undef, flw_steps-1) global dEcldt = Vector{uwreal}(undef, flw_steps-1) global dflwtime = Vector{Float64}(undef, flw_steps-1) # t0pl,t0spl = interpolate_root_s(0.0036, flwtime, E, ID, true) t0pl,t0spl = interpolate_root_s(0.0036, flwtime, Ecl, ID, true) t0oL = sqrt(8*t0pl)/lat uwerr(t0oL) print("\n sqrt{8t0}/L:\t",t0oL) #+end_src #+RESULTS: :RESULTS: : ID: simulations/h2_2_16x16x16x24_beta5.5_k10.1324_k20.1324_etas0.001_0.01_mu0.005_xis0.0001_0.0001_0.0005_0.0005_niter8000_eps0.05_nsteps35_SM_us_20_0.01_ss_60_0.1.bdio10008000251080020600.005Group: SU2{Float64} : - beta: 5.5 : - c0: 1.0 : - cG: (0.0, 0.0) : Number of scalar fields: 2 : - Kappas: 0.1324 0.1324 0.001 0.01 0.005 (0.0001, 0.0001, 0.0005, 0.0005)100 : DB length: 7901 : obs length: 8000 # [goto error] #+begin_example Mistmatch in data length for the same ensemble ID Stacktrace: [1] error(s::String) @ Base ./error.jl:33 [2] add_maps(id::Int64, ws::ADerrors.wspace, iv::Vector{Int64}) @ ADerrors ~/.julia/packages/ADerrors/yII7Y/src/ADerrorsCF.jl:128 [3] add_DB(delta::Vector{Float64}, id::Int64, iv::Vector{Int64}, ws::ADerrors.wspace, do_maps::Bool) @ ADerrors ~/.julia/packages/ADerrors/yII7Y/src/ADerrorsCF.jl:178 [4] add_DB @ ~/.julia/packages/ADerrors/yII7Y/src/ADerrorsCF.jl:144 [inlined] [5] uwcls_gaps(data::Vector{Float64}, id::Int64, ws::ADerrors.wspace, iv::Vector{Int64}, idm::Vector{Int64}, nms::Int64) @ ADerrors ~/.julia/packages/ADerrors/yII7Y/src/ADerrorsCF.jl:228 [6] uwreal(data::Vector{Float64}, str::String, iv::Vector{Int64}, idm::Vector{Int64}, nms::Int64) @ ADerrors ~/.julia/packages/ADerrors/yII7Y/src/ADerrorsCF.jl:932 [7] top-level scope @ ./In[7]:14 [8] eval @ ./boot.jl:373 [inlined] [9] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String) @ Base ./loading.jl:1196 #+end_example :END: *** Numerical derivative $W(t) = t \dv{}{t}\left( t^2E(t) \right)$ **** Adapt. GF #+begin_src jupyter-julia :session h2ps dflwtime, dEcldt, C = timederiv(flwtime, Ecl, flw_dt,wpm) w0pl,w0spl = interpolate_root_s(-0.00066, dflwtime, dEcldt, ID, true) #+end_src **** Full GF #+begin_src jupyter-julia :session h2ps for i in 2:flw_steps # dEdt[i-1] = (flwtime[i]-(flw_dt*flw_s/2.0))*(E[i] - E[i-1])/(flw_dt*flw_s) # uwerr(dEdt[i-1], wpm) # print(i," ") dEcldt[i-1] = (flwtime[i]-(flw_dt*flw_s/2.0))*(Ecl[i] - Ecl[i-1])/(flw_dt*flw_s) dflwtime[i-1] = flwtime[i]-(flw_dt*flw_s/2.0) uwerr(dEcldt[i-1], wpm) # try # catch e # @warn string(": ill error propagation at t_index = ", i) # end end # w0pl,w0spl = interpolate_root(-0.001, dflwtime, dEdt, ID, true) w0pl,w0spl = interpolate_root(-0.00066, dflwtime, dEcldt, ID, true) #+end_src #+RESULTS: :RESULTS: : : chi^2 / d.o.f.: : 1.6218543362323661e-15 / 0 = Inf : Distance of interpolation: 0.0400000000000027 : Root: : 17.199756453647524 +/- 0.16620421079631298 [[file:./.ob-jupyter/1eb2b87a0d2460af64add26718afbbdc31565340.svg]] : [-3.88420509267462e-6] :END: *** Correlation functions **** $m_H$ ***** Average & Plot #+begin_src jupyter-julia :session h2ps L=length(h2[1,:,1]) ttl = latexstring("\\kappa_1 = \\kappa_2 = $(sp.kap[1])") xlbl = L"t" ylbl = L"C(t)" Chiggs = Array{uwreal,2}(undef, 2, L) p=plot(reuse=false) for hgs in 1:2 Chiggs[hgs,:] = correlator(h2[hgs,:,thm:end], tag, niter_vec .- (thm-1)) for t in 1:L Chiggs[hgs,t] *= 1/(Chiggs[hgs,end]) uwerr(Chiggs[hgs,t]) end lbl = latexstring("H_$(hgs)") p=plot!(value.(Chiggs[hgs,:]),yerr=err.(Chiggs[hgs,:]), ylabel=ylbl, xlabel=xlbl,label=lbl, title=ttl) end display(p) # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/corrfunc_h1_h2_k1_$(sp.kap[1])_k2_$(sp.kap[2])_lat$(lat)x$(tim)_b$(bet).png" # print(outputname) # savefig(outputname) #+end_src #+RESULTS: [[file:./.ob-jupyter/9ba61f71b72a1891f7ed909c1a2215883a632c69.svg]] ***** Effective Mass #+begin_src jupyter-julia :session h2ps emass = Array{uwreal,2}(undef,2, L-3) l2 = convert(Int64, L/2-1)-0 p=plot(reuse=false) for hgs in 1:2 emass[hgs,:], ind = effsymmass(Chiggs[hgs,:]) p=plot!(value.(emass[hgs,begin:l2]),yerr=err.(emass[hgs,begin:l2])) end display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/a5ad0951847197bcadafc5cf5764c47795003097.svg]] ***** Mass extraction #+begin_src jupyter-julia :session h2ps @.funn(x,p) = p[1] + x*0.0 mHvec = Vector{uwreal}(undef,2) sysHvec = Vector{uwreal}(undef,2) for hgs in 1:2 emass, indH = effsymmass(Chiggs[hgs,:]) l2 = length(indH) tstart = collect(1:l2) tend = collect(2:l2) (mH,sysH) = bayesian_av(funn, emass[indH], tstart, tend, 1, plot_bayesian=true) mH = mH + uwreal([0.0,value(sysH)], "systematic uncertainty") uwerr(mH) mHvec[hgs] = mH sysHvec[hgs] = sysH end for hgs in 1:2 print("Model average - Effective Mass:\n",mHvec[hgs],"\n",sysHvec[hgs]) end # mH=mH[1] #APAGAR ISTO DEPOIS # sysH = sysH[1] #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/551d3087d3543cf940853223898d35dfa14cd1da.svg]] [[file:./.ob-jupyter/c7a6eb9b7442e192eca2d17cc18fca3451dc7a7c.svg]] [[file:./.ob-jupyter/d8fd5d9b22c9bfa3b1d4e57784dd59207f1e96f5.svg]] [[file:./.ob-jupyter/c5dee4a57b408ea2463d896103b6f86175e5d938.svg]] [[file:./.ob-jupyter/a7ccc88970c943c351db57de1c8578e75997c15a.svg]] [[file:./.ob-jupyter/d857aadde1187a9500478b15a68c610995bd9e68.svg]] : Model average - Effective Mass: : 0.5275947747557567 +/- 0.09519505426442436 : 0.08600046214506923 (Error not available... maybe run uwerr)Model average - Effective Mass: : 0.3484659435818859 +/- 0.0783437948455635 : 0.06828067355826857 (Error not available... maybe run uwerr) :END: **** $m_w$ ***** $W_{\mu,i}$ #+begin_src jupyter-julia :session h2ps #choose window wpm = Dict{String, Vector{Float64}}() # ALWAYS ATTEMPT TO RUN WITHOUT CUSTOM wpm # wpm[tag] = [50.0, -1.0, -1.0, -1.0] hgs = 1 CW = Array{uwreal,4}(undef, 3, 3, 3, tim) for hgs in 1:3 for mu in 1:3 for i in 1:3 CW[hgs,mu,i,:] = correlator(w1[hgs,mu,i,:,thm:end], tag, niter_vec .- (thm-1),wpm) for t in 1:tim CW[hgs, mu,i,t] *= 1/(CW[hgs, mu,i,end]) uwerr(CW[hgs, mu,i,t]) end end end end #average over mu and i CWmean = Array{uwreal,2}(undef, 3, tim) for hgs in 1:3 for t in 1:tim CWmean[hgs,t] = sum(CW[hgs,:,:,t])/(3*3) uwerr(CWmean[hgs,t]) end end #+end_src #+RESULTS: ****** Plot $W_{\mu,i}$ #+begin_src jupyter-julia :session h2ps plot(reuse=false) for mu in 1:3 for i in 1:3 p=plot!(value.(CW[mu,i,st:end]),yerr=err.(CW[mu,i,st:end]), ylabel=ylbl, xlabel=xlbl, label=latexstring("W_{$(mu),$(i)}(t)")) end end display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/0e9e19147a53c04ed57b2cec3b21ed5765568d57.svg]] ****** Plot Average in $\mu,i$ #+begin_src jupyter-julia :session h2ps plot(reuse=false) ttl = latexstring("\\kappa_1 = \\kappa_2 = $(sp.kap[1])") xlbl = L"t" ylbl = L"C(t)" p=plot(reuse=false) for hgs in 1:3 lbl = latexstring("W_$(hgs)") p=plot!(value.(CWmean[hgs,1:end]),yerr=err.(CWmean[hgs,1:end]), ylabel=ylbl, xlabel=xlbl, label=lbl,title=ttl) end display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/corrfunc_w1_w2_k1_$(sp.kap[1])_k2_$(sp.kap[2])_lat$(lat)x$(tim)_b$(bet).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/622ec150b24b74a391d658ab6d16c9508b93ec64.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/corrfunc_w1_w2_k1_0.133_k2_0.1307_lat24x24_b5.5.png :END: ***** Effective Mass #+begin_src jupyter-julia :session h2ps Wemass = Array{uwreal,2}(undef, 3, L-3) p=plot(reuse=false) for hgs in 1:3 l2 = convert(Int64, L/2-1)-0 Wemass[hgs,:],indW = effsymmass(CWmean[hgs,:]) p=plot!(value.(Wemass[hgs,begin:l2]),yerr=err.(Wemass[hgs,begin:l2])) end display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/1c8e08aca7858d8e55e833406a54095843062a99.svg]] ***** Mass extraction #+begin_src jupyter-julia :session h2ps mWvec = Vector{uwreal}(undef,3) sysWvec = Vector{uwreal}(undef,3) @.funn(x,p) = p[1] + x*0.0 for hgs in 1:3 Wemass,indW = effsymmass(CWmean[hgs,:]) l2 = length(indW) tstart = collect(1:l2) tend = collect(2:l2) (mW,sysW) = bayesian_av(funn, Wemass[indW], tstart, tend, 1, plot_bayesian=true) mW = mW + uwreal([0.0,value(sysW)], "systematic uncertainty") uwerr(mW) mWvec[hgs] = mW sysWvec[hgs] = sysW end for hgs in 1:3 print("Model average - Effective Mass:\n",mWvec[hgs],"\n",sysWvec[hgs]) end #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/725de978af1d922cee9bb7ba929ce43f59b747d1.svg]] [[file:./.ob-jupyter/7b96c5774c90509fd61d0dcadd3e4a9e5d0896f3.svg]] [[file:./.ob-jupyter/543467caa3dc4fb13ab2ace37b4cb5f4401dcc5d.svg]] [[file:./.ob-jupyter/64210f44e5382e0c229b32cd643a523d10fc2c18.svg]] [[file:./.ob-jupyter/76d5eb59a5c6373ff93a336fa6f4e167a633deda.svg]] [[file:./.ob-jupyter/64c7525ff140441dbbdf5c81a002b4b5a0b0e9f3.svg]] [[file:./.ob-jupyter/db159a38392d7fb96d1ae9f8ed0a99abef5ff1c0.svg]] [[file:./.ob-jupyter/45089f338613d016bb76a659c58a9ccee53a53ca.svg]] [[file:./.ob-jupyter/587855736668f9f3d4270054f9d68f745d3e18c4.svg]] : Model average - Effective Mass: : 0.5027293997421788 +/- 0.013874664433300472 : 0.009946717489634107 (Error not available... maybe run uwerr)Model average - Effective Mass: : 0.4981406113311257 +/- 0.012477947064081464 : 0.010182442956749768 (Error not available... maybe run uwerr)Model average - Effective Mass: : 0.3140066229415764 +/- 0.01491577376244547 : 0.006677459600208518 (Error not available... maybe run uwerr) :END: ** Simulations & Plots *** Plot Gradient Flow **** Plaquette ***** E(t) #+begin_src jupyter-julia :session h2ps plot(reuse=false) s=1 for i in 1:length(flwtime) if flwtime[i]>2.0 # s=i break end end c=flw_steps ttl=latexstring("\\beta=$(beta),~k_1=k_2=$(k1)") xax = latexstring("t/a^2") yax = latexstring("t^{2}<E(t)>") # plot(reuse=false) f(x)=0.0036 p=plot!(f,flwtime[s],flwtime[c]) p=scatter!(flwtime[s:c],value.(E[s:c]),yerror=err.(E[s:c]),label="Plaq.",title=ttl, xlabel=xax,ylabel=yax, legend=:topleft) # p=scatter!(flwtime[s:c],value.(Ecl[s:c]),yerror=err.(Ecl[s:c]), label="Clover")#,ylims=(0.0005,0.0075) display(p) outputname="reports/figs/GF_k1k2$(k1)_beta$(beta)_$(lat)to4.png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/5683b881e0a7fa3426cc5a287614b788bff54417.svg]] : reports/figs/GF_k1k20.1298_beta9.1_20to4.png :END: ***** dE(t) #+begin_src jupyter-julia :session h2ps #numerical derivative plot(reuse=false) s=1 c=100 q = scatter!(dflwtime[s:end], value.(dEdt[s:end]),yerr=err.(dEdt[s:end]),label="Numerical - midpoint - Plaq.") # q = scatter!(dflwtime[s:end], value.(dEcldt[s:end]),yerr=err.(dEcldt[s:end]),label="Numerical - midpoint - Clover") f(x)=-0.001 q = plot!(f, 0, dflwtime[end]) display(q) #+end_src #+RESULTS: [[file:./.ob-jupyter/a29056e81ce8c0c2a128b8c1158804ffd4cd4420.svg]] ***** Extraction of $t_0$ #+begin_src jupyter-julia :session h2ps t0pl,t0spl = interpolate_root(0.0036, flwtime, E, ID, true) #+end_src #+RESULTS: :RESULTS: : : chi^2 / d.o.f.: : 1.056305073614457e-19 / 0 = Inf : Distance of interpolation: 0.20999999999999996 : Root: : 4.326324465315498 +/- 0.02828546755021743 [[file:./.ob-jupyter/c435c8a9733e4c08c3c267c272bff932b34f376b.svg]] : [0.00043613505600337277] :END: ***** Extraction of $w_0$ Find Fit limits #+begin_src jupyter-julia :session h2ps c=0.2 f(x)=c #find time index corresponding to max E(t) < c tb = 0 for i in 1:flw_steps-1 if (value(dEdt[i]) < c && value(dEdt[i+1]) > c) tb = i end end # 6 fit points: 3 below, 3 above xmin=tb-2 xmax=tb+3 p=plot(flwtime[xmin:xmax], value.(dEdt[xmin:xmax]),yerr=err.(dEdt[xmin:xmax])) p=scatter!(flwtime[xmin:xmax], value.(dEdt[xmin:xmax]),yerr=err.(dEdt[xmin:xmax]),legend=:bottomright) p=plot!(f,flwtime[xmin],flwtime[xmax]) display(p) #+end_src #+RESULTS: [[file:./.ob-jupyter/a071eb90b333a8fef9a4df40de7ae933b8e55a3e.svg]] Linear fit $y(x)=ax+b$ #+begin_src jupyter-julia :session h2ps print("PLAQUETTE\n") #linear function @. linfunc(x, p) = p[1]*x + p[2] #xmin, xmax xdata = flwtime[xmin:xmax] ydata = value.(dEdt[xmin:xmax]) dydata = err.(dEdt[xmin:xmax]) #bounds for parameters p[i] #lower bound lb = [0.0, -Inf] #upper bound ub = [Inf, Inf] #starting values p0_bounds = [0.1, -0.1] #fit fit_bounds = curve_fit(linfunc, xdata, ydata, p0_bounds, lower=lb, upper=ub) print("D.O.F.: ",dof(fit_bounds),"\na,b: ",coef(fit_bounds),"\n") #chisquared chisq(p, d) = sum( (d .- ( linfunc(xdata,p) ) ) .^ 2 ./ (dydata) .^2 ) # t0 print("t_0: ",(c - coef(fit_bounds)[2])/coef(fit_bounds)[1]) #propagate (fitp, csqexp) = fit_error(chisq, coef(fit_bounds), dEdt[xmin:xmax]) chi = chisq(coef(fit_bounds),ydata) print("\nchi^2 / d.o.f.: \n\t", chi, " / ", dof(fit_bounds), " = $(chi/dof(fit_bounds))\n") a = fitp[1] b = fitp[2] # t_0 = (0.1 - b)/a t0 = (c - b)/a uwerr(t0) t0plq_vol[1] = t0 print("FINAL w_0^2:\n\t",t0) st0 = sqrt(t0) uwerr(st0) print("\nFINAL w_0:\n\t",st0) sst0 = sqrt(st0) uwerr(sst0) print("\nFINAL sqrt(w_0):\n\t",sst0) #+end_src #+RESULTS: #+begin_example PLAQUETTE D.O.F.: 4 a,b: [0.05797578884293657, -0.08117864230366377] t_0: 4.849932151253876 chi^2 / d.o.f.: 0.022836833607156284 / 4 = 0.005709208401789071 FINAL w_0^2: 4.849932151253876 +/- 0.018764735781375927 FINAL w_0: 2.2022561502363605 +/- 0.004260343597942041 FINAL sqrt(w_0): 1.484000050618719 +/- 0.0014354256915846439 #+end_example Add to Volume arrays #+begin_src jupyter-julia :session h2ps push!(w0plq_vol,t0) #+end_src #+RESULTS: **** Clover ***** E(t) #+begin_src jupyter-julia :session h2ps plot(reuse=false) f(x)=0.0036 k=1 for i in 1:length(flwtime) if flwtime[i]>1.0 k=i break end end # p=plot!(f,flwtime[k],flwtime[end],legend=:bottomright) # p=scatter!(flwtime[k:end],value.(Ecl[k:end]),yerror=err.(Ecl[k:end]),label="Clover") p=scatter!(flwtime[k:end],value.(Ecl[k:end]),yerror=err.(Ecl[k:end]),label="Clover") # p=scatter!(flwtime,value.(E[begin:end]),yerror=err.(E[begin:end]),label="Plaq.") display(p) outputname = "/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/GF_beta$(beta)_kappa$(k1)_l$(eta).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/2a35423c2368ad56b1fd43ff06c13aaba85a1e70.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/GF_beta9.1_kappa0.12967_leta.png :END: ***** dE(t)/dt #+begin_src jupyter-julia :session h2ps #numerical derivative plot(reuse=false) s=1 c=100 f(x) = -0.001 q = scatter!(dflwtime[s:end], value.(dEcldt[s:end]),yerr=err.(dEcldt[s:end]),label="Numerical - midpoint - Clover") q = plot!(f,dflwtime[s],dflwtime[end]) display(q) #+end_src #+RESULTS: [[file:./.ob-jupyter/a8b50dd51fa69d6e89858aa4290d8b18fe7ad20c.svg]] *** Save simulation $(\beta,\kappa,\lambda)$ **** Reset Vector #+begin_src jupyter-julia :session h2ps :results output silent runs = Vector{h2runs}() #+end_src **** Save measurement #+begin_src jupyter-julia :session h2ps t0vec = Vector{uwreal}(undef,1) w0vec = Vector{uwreal}(undef,1) E = Vector{uwreal}(undef,1) Ecl = Vector{uwreal}(undef,1) a = h2sim(gp, sp, mHvec, mWvec, t0vec, w0vec, flwtime, E, Ecl, glbvec,dims,smear) # add run to vector # first check if this beta is already there append = true rr = sims for i in 1:length(rr) rgp = rr[i].gp rsp = rr[i].sp if rgp.beta == gp.beta && rsp.kap[1] == sp.kap[1] && rsp.kap[2] == sp.kap[2] && rsp.eta[1] == sp.eta[1] && rsp.eta[2] == sp.eta[2] && rsp.muh == sp.muh && rsp.xi[1] == sp.xi[1] && rsp.xi[2] == sp.xi[2] && rsp.xi[3] == sp.xi[3] && rsp.xi[4] == sp.xi[4] rr[i] = a append = false end end if append push!(rr, a) print("append") end #+end_src #+RESULTS: : append ***** Copy vector without repetitions #+begin_src jupyter-julia :session h2ps newruns = Vector{h2runs}() unique_ks = [] for n in runs k = n.sp.kap[1] sv = false if k in unique_ks sv = false else sv = true end if sv push!(unique_ks, k) push!(newruns,n) end end #+end_src #+RESULTS: #+begin_src jupyter-julia :session h2ps print(length(newruns)) #+end_src #+RESULTS: : 9 **** TODO (update) Print info #+begin_src jupyter-julia :session h2ps for n in runs if n.bt == b && n.k == k && n.l == l global mH = n.mH global mW = n.mW a = sqrt(8*n.t0)*n.mH uwerr(a,wpm) b = sqrt(8*n.t0)*n.mW uwerr(b,wpm) c = n.mH/n.mW uwerr(c,wpm) uwerr(n.mH,wpm) uwerr(n.mW,wpm) uwerr(n.t0) uwerr(n.w0) print("old t0\t:",n.t0) print("\nnew t0\t:",t0pl) print("\nold w0\t:",n.w0) print("\nnew w0\t:",w0pl) d = w0pl/t0pl uwerr(d,wpm) uwerr(t0pl,wpm) uwerr(w0pl,wpm) mWL = mW*lat uwerr(mWL) gg = sqrt(8*t0pl)/lat uwerr(gg) print("\n",n.bt, " | ", n.k, " | ", n.l , " | ", a, " | ", b, " | ",c , " | ", n.mH, " | ", n.mW, " | ", d, " | ", t0pl, " | ", w0pl, " | ", mWL, " | ", gg) end end #+end_src #+RESULTS: : old t0 :9.165930974618949 +/- 0.11002790028464296 : new t0 :9.165930974618936 +/- 0.10322476440146872 : old w0 :15.776620491832308 +/- 0.11942887368449177 : new w0 :11.254329531219438 +/- 0.08595226551577581 : 8.8 | 0.1301 | 0.0032 | 1.611(89) | 0.941(55) | 1.713(88) | 0.188(10) | 0.1099(64) | 1.228(11) | 9.17(10) | 11.254(86) | 3.52(20) | 0.2676(15) **** TODO (update) Remove measurement #+begin_src jupyter-julia :session h2ps rm = true kp = 0.1315 for i in 1:length(runs) if (runs[i].sp).kap[1] == kp print((runs[i].sp).kap,"\n") # deleteat!(runs, i) end end #+end_src #+RESULTS: #+begin_src jupyter-julia :session h2ps for n in runs print(n,"\n") end #+end_src #+RESULTS: #+begin_example h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.133 0.133 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.3266602619173014 +/- 0.043305315572180904, 0.3398703401743157 +/- 0.04709181286023165], uwreal[0.4097991295579195 +/- 0.009612187019466317, 0.4135236051061287 +/- 0.013719725833586181, 0.7838032424207018 +/- 0.14639252297225644], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.134 0.134 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5247241693044553 +/- 0.08043321210811183, 0.5600932889910963 +/- 0.04489114365671268], uwreal[0.6113483052326071 +/- 0.07303251522424217, 0.5968202954776942 +/- 0.03964185813731235, 1.0167882202423273 +/- 0.10594595681276923], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1345 0.1345 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5161410487332492 +/- 0.13732389761374, 0.6671936445705801 +/- 0.0649084208275662], uwreal[0.6421539115807234 +/- 0.03359802483591346, 0.643203246299235 +/- 0.035489003635800334, 0.9104955145826528 +/- 0.11661373393488784], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.135 0.135 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6833659953411675 +/- 0.19189543489279884, 0.7936722915883625 +/- 0.1645107097856962], uwreal[0.6949299419795532 +/- 0.04093822120877493, 0.6920154749974967 +/- 0.03844657466677778, 0.9445195904855038 +/- 0.051353773476049594], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1355 0.1355 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6832377253446192 +/- 0.0537213310577472, 0.75565443496379 +/- 0.18228246186528813], uwreal[0.7705869340495459 +/- 0.048963100043085676, 0.7768407134608469 +/- 0.03982568826831638, 1.0627720009920576 +/- 0.18455841729750172], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.136 0.136 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7381970051547233 +/- 0.17030346918733455, 0.7383813442525676 +/- 0.1329308216559864], uwreal[0.8560123544848621 +/- 0.11300186635874171, 0.8794888589847879 +/- 0.14386095370613644, 1.014029543055941 +/- 0.07257451124476401], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1325 0.1325 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.2073439255915157 +/- 0.020507319848849077, 0.2658166650167719 +/- 0.04695603175647445], uwreal[0.2589628746146604 +/- 0.00694335700333698, 0.26711261945214226 +/- 0.012799921666165925, 0.9192827816283349 +/- 0.19534857054845028], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1318 0.1318 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.6742417956351455 +/- 0.31349310709383615, 1.0904882424054887 +/- 0.2245139131021689], uwreal[1.2828806654768519 +/- 0.44263400094310823, 1.4268749256558169 +/- 1.9985581048737624, 1.4268749256558169 +/- 1.9985581048737624], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1322 0.1322 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.23195738082338238 +/- 0.03933186735296764, 0.4257900827744041 +/- 0.08037735651129799], uwreal[0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743, 0.8889583750572612 +/- 0.09558585229971743], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1328 0.1328 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.33586592684449856 +/- 0.06700517915174804, 0.3284966150688644 +/- 0.05956254215553113], uwreal[0.3393422458923846 +/- 0.009363259790719261, 0.34791876675004535 +/- 0.009133466158336977, 0.96135230176371 +/- 0.28515849693195144], uwreal[], uwreal[], Float64[], uwreal[], uwreal[]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1315 0.1315 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.7034313637344539 +/- 0.10301758959839283, 0.914034777418048 +/- 0.2447899199449841], uwreal[0.8894021486084969 +/- 0.5258404416709271, 1.7438680449057962 +/- 0.20312410591738433, 1.5345206062503527 +/- 0.22319019320713085], uwreal[#undef], uwreal[#undef], [6.95104524557654e-310, 6.95100875756537e-310, 6.95104524546113e-310, 6.9510452454627e-310, 6.9510452454643e-310, 6.95104524546587e-310, 6.95104524546903e-310, 6.9510452454706e-310, 6.95104524547377e-310, 6.9510452454801e-310, 6.9510452454817e-310, 6.95104524548326e-310, 6.9510452454864e-310, 6.9510452552381e-310, 6.9511414704713e-310, 6.95100875788315e-310, 6.951045245488e-310, 6.9510087576966e-310, 6.9511490148604e-310, 6.9510452455038e-310, 6.9510452455117e-310, 6.95104524552595e-310, 6.95104524554176e-310, 6.95100875981673e-310, 0.0], uwreal[#undef], uwreal[#undef]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.132 0.132 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.5489508260234942 +/- 0.0661122114216399, 0.9447180505985463 +/- 0.14264822786791875], uwreal[0.8849591765891759 +/- 0.07457940844020193, 1.24160729026234 +/- 0.45825346280051754, 0.9447180505985463 +/- 0.14264822786791875], uwreal[#undef], uwreal[#undef], [-0.008127751098824102, 0.000828110634833442, -0.002889906438474466, 0.0009104944128808163, 0.00384785124580064, -0.002982834444909155, 0.005928740294683068, -0.00725563800474393, -0.01198399014676348, -0.0010500787271761362, 0.005111270108291018, 0.0017900073582518246, 0.0003365890318593467, 3.057624960631588e-5, 0.0027802919920645536, 0.001456761142657014, -0.007261427898294045, -0.002536740310277763, -0.0018033895720405422, -0.007031479885340497, -0.0006106686260242858, -0.000343489196675588, 0.004870644789895715, -0.005711289102333973, 0.0], uwreal[#undef], uwreal[#undef]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1327 0.1327 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.27170443016805573 +/- 0.02716371479605193, 0.34936997858120783 +/- 0.08107658974764084], uwreal[0.31889546071944147 +/- 0.00585209962010578, 0.3324607531296575 +/- 0.008498156710565804, 0.7646804325478338 +/- 0.13527321894645059], uwreal[#undef], uwreal[#undef], [2.1219957915e-314, 2.1219957915e-314, 2.1219957915e-314, 2.1219957915e-314, 2.1219957915e-314, 2.1219957915e-314, 6.3659873734e-314, 6.3659873744e-314, 8.4879831653e-314, 8.487983166e-314, 8.487983166e-314, 8.487983166e-314, 8.487983166e-314, 1.06099789573e-313, 1.06099789573e-313, 1.48539705397e-313, 1.485397054e-313, 1.485397054e-313, 1.485397054e-313, 1.6975966331e-313, 1.69759663317e-313, 1.69759663317e-313, 1.69759663317e-313, 2.12199579136e-313, 0.0], uwreal[#undef], uwreal[#undef]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1324 0.1324 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.1710457337782237 +/- 0.016683160708291834, 0.22288558978679684 +/- 0.045576750464175954], uwreal[0.2179068545264541 +/- 0.005817623980632494, 0.2378861323912228 +/- 0.014077801695814955, 0.9057809605692666 +/- 0.12981509673569563], uwreal[#undef], uwreal[#undef], [0.0, 6.95113812778913e-310, 6.95113810577435e-310, 0.0, 6.95113812778913e-310, 6.95114097557402e-310, 0.0, 6.95114097556296e-310, 6.95114097556296e-310, 0.0, 6.9511409755598e-310, 6.9511409755598e-310, 0.0, 6.95114097555663e-310, 6.95114097555663e-310, 0.0, 6.95113812778913e-310, 6.95113810577435e-310, 0.0, 6.95113812778913e-310, 6.95113810577435e-310, 0.0, 6.95113812778913e-310, 6.95113810577435e-310, 0.0], uwreal[#undef], uwreal[#undef]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1323 0.1323 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.12508383165279127 +/- 0.020269171659453907, 0.1367845784064862 +/- 0.018086069353228696], uwreal[0.17327214520650672 +/- 0.0061544388057530805, 0.24980003188108987 +/- 0.06010711358492358, 0.8564757107856912 +/- 0.18128705760204095], uwreal[#undef], uwreal[#undef], [0.0, 6.95113812778913e-310, 6.9511387450771e-310, 0.0, 6.9511490164869e-310, 6.9511490164869e-310, 0.0, 6.95114018825394e-310, 6.95114018825394e-310, 0.0, 6.95113812778913e-310, 6.95113810577435e-310, 0.0, 6.95113812778913e-310, 6.95114018835117e-310, 0.0, 6.95113812778913e-310, 6.9511381871837e-310, 0.0, 6.95113812778913e-310, 6.95113810577435e-310, 0.0, 6.95113812778913e-310, 6.95113810577435e-310, 0.0], uwreal[#undef], uwreal[#undef]) h2runs{Float64}(Group: SU2{Float64} - beta: 6.0 - c0: 1.0 - cG: (0.0, 0.0) , Number of scalar fields: 2 - Kappas: 0.1326 0.1326 - etas: 0.01 0.05 - mu12: 0.01 - xi: (0.005, 0.005, 0.01, 0.01) , uwreal[0.228988694882946 +/- 0.022846774075704804, 0.28332638134015736 +/- 0.027191266294877576], uwreal[0.29481349727941925 +/- 0.006217980815922042, 0.3132613293834663 +/- 0.00994857104952149, 0.8434542356450345 +/- 0.2072933710846273], uwreal[#undef], uwreal[#undef], [1.182994722033473, 1.154840556761539, 1.1723602224478837, 1.2034056361944352, 1.2216597174664279, 1.1551367566559625, 1.1790404254946676, 1.1934347224807826, 1.2100423156279225, 1.2025872137109053, 1.1858175291175066, 1.2007589116003747, 1.2107955035760083, 1.2345259910571194, 1.2047613707589944, 1.1770983771477395, 1.1345715216474033, 1.1133502674132665, 1.1037581931943703, 1.136643623193269, 1.1589649082989948, 1.1824135955210129, 1.19006636837246, 1.203126163472724, 6.95113810577435e-310], uwreal[#undef], uwreal[#undef]) #+end_example **** Save BDIO #+begin_src jupyter-julia :session h2ps :results output silent save = true # save = false if save write_cruns_bdio("saved_sim/su2_phasespace_simulations_glb_dims.bdio", sims) end #+end_src **** Read BDIO #+begin_src jupyter-julia :session h2ps read = true #test reading runs if read sims = read_cruns_bdio("saved_sim/su2_phasespace_simulations_glb_dims.bdio") end #+end_src #+RESULTS: #+begin_example ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 ┌ Warning: No flowtime └ @ Main In[2]:759 - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: - etas: - mu12: - xi: #+end_example **** Print simulation details #+begin_src jupyter-julia :session h2ps rr = newsims for r in rr # print(r.gp,"\n",r.sp,"\n\n") end #+end_src #+RESULTS: **** Plots - $\kappa_1=\kappa_2$ ***** Tables #+begin_src jupyter-julia :session h2ps bet = 6.0; et1=0.01; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 ks = Vector{Float64}() rr = runs for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(ks,rsp.kap[1]) end end sort!(ks) L=24 LmHs = Array{uwreal,2}(undef, 2, length(ks)) LmWs = Array{uwreal,2}(undef, 3, length(ks)) plt = plot(reuse=false, xlabel=L"k",ylabel=ylbl, title=ttl,) rr = runs for i in 1:length(ks) k=ks[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.kap[1] == k && rsp.kap[2]==k print(k, " & ") LmHs[:,i] = r.mH*L uwerr.(LmHs[:,i]) for h in 1:length(LmHs[:,i]) print(LmHs[h,i]," & ") end LmWs[:,i] = r.mW*L uwerr.(LmWs[:,i]) for h in 1:length(LmWs[:,i])-2 print(LmWs[h,i]," & ") end print(LmWs[end-1,i]," \\\\ ") print("\n") end end end #+end_src #+RESULTS: #+begin_example 0.132 & 14.470003130131833 +/- 3.3072176771200588 & 22.88387286992193 +/- 5.1969310870367345 & 21.04368102169744 +/- 2.0824780708894917 & 31.50946223409074 +/- 14.89656217985309 \\ 0.1322 & 5.683665292957693 +/- 0.8294259899696295 & 11.068897107316968 +/- 1.8034923346846474 & 20.74026238560398 +/- 2.439604258812843 & 20.74026238560398 +/- 2.439604258812843 \\ 0.13222 & 1.3400392350595223 +/- 0.16894541700432145 & 3.211486448335398 +/- 0.5867881988352427 & 9.945853952134023 +/- 1.7162370525980872 & 20.220353389892754 +/- 6.617003348758613 \\ 0.13223 & 1.6204041802451683 +/- 0.23148129908107345 & 2.521949063726735 +/- 0.39248351436508655 & 4.381286519106243 +/- 0.7461263940118501 & 13.586846439687433 +/- 3.325607163381904 \\ 0.13224 & 1.4436864624031462 +/- 0.12004165866355693 & 2.941969086913296 +/- 0.6076050404779466 & 3.734430873140795 +/- 0.6711585902304625 & 11.573202259615826 +/- 2.072917208159276 \\ 0.13225 & 1.95(13) & 2.86(31) & 4.17(46) & 8.5(16) \\ 0.1323 & 2.7451319081499395 +/- 0.5368877415849318 & 3.180877122084287 +/- 0.48731956581963787 & 4.119033714020283 +/- 0.15506943567716472 & 6.433554584659282 +/- 1.1560182396803018 \\ 0.13235 & 3.3382596004952245 +/- 0.2965944344493117 & 5.008714008126448 +/- 0.633057896326169 & 4.539745565774503 +/- 0.17403235987341512 & 5.716735302522827 +/- 0.5134140961574658 \\ 0.1324 & 4.136751545332277 +/- 0.3334915844549371 & 5.735139246118315 +/- 0.8778743752464501 & 5.210574956379992 +/- 0.13393051690850594 & 5.695720924948221 +/- 0.3251005586120681 \\ 0.1325 & 4.925022606229531 +/- 0.4610944851322006 & 7.8734630801001995 +/- 1.0874555182123968 & 6.2034964472548255 +/- 0.16473644177211086 & 6.446034462877304 +/- 0.3162824614379969 \\ 0.1326 & 5.76560712222295 +/- 0.5925587047977615 & 7.231572734129321 +/- 0.6520162501062319 & 7.082456665799546 +/- 0.15327340839492729 & 7.514953286258399 +/- 0.2720412641688018 \\ 0.1328 & 7.266947742407748 +/- 0.6864342917340789 & 8.396712897406356 +/- 1.1512354458709761 & 8.187105511400791 +/- 0.24530529754040487 & 8.385689009130102 +/- 0.2579485136912785 \\ #+end_example ***** $aM_W(\kappa)$ #+begin_src jupyter-julia :session h2ps bet = 6.0; et1=0.01; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 ttl = latexstring("\\beta=$(bet);~k_1=k_2") ylbl = latexstring("am") ks = Vector{Float64}() rr = sims for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(ks,rsp.kap[1]) end end sort!(ks) mHs = Array{uwreal,2}(undef, 2, length(ks)) mWs = Array{uwreal,2}(undef, 3, length(ks)) plt = plot(reuse=false, xlabel=L"k",ylabel=ylbl, title=ttl,) for i in 1:length(ks) k=ks[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.kap[1] == k && rsp.kap[2]==k mHs[:,i] = r.mH uwerr.(mHs[:,i]) mWs[:,i] = r.mW uwerr.(mWs[:,i]) end end end st=3 fc=0 p=scatter!(ks[st:end-fc], value.(mHs[1,st:end-fc]), yerr=err.(mHs[1,st:end-fc]), label=L"m_{H_1}", markershape=:utriangle)#,xlims=(0.1319,0.1331)) # p=scatter!(ks[st:end-fc], value.(mHs[2,st:end-fc]), yerr=err.(mHs[2,st:end-fc]), label=L"m_{H_2}") p=scatter!(ks[st:end-fc], value.(mWs[1,st:end-fc]), yerr=err.(mWs[1,st:end-fc]), label=L"m_{W_1}", markershape=:diamond) # p=scatter!(ks[st:end-fc], value.(mWs[2,st:end-fc]), yerr=err.(mWs[2,st:end-fc]), label=L"m_{W_2}") # p=scatter!(ks[st:end-fc], value.(mWs[3,st:end-fc]), yerr=err.(mWs[3,st:end-fc]), label=L"m_{W_{12}}") print(ks[st:end-fc]) display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mH2_lat$(lat)x$(tim)_b$(bet).png" outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mW1_lat$(lat)x$(tim)_b$(bet).png" outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mW1_mW2_lat$(lat)x$(tim)_b$(bet).png" outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mW12_lat$(lat)x$(tim)_b$(bet).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: : [0.13222, 0.13223, 0.13224, 0.13225, 0.1323, 0.13235, 0.1324, 0.1325, 0.1326, 0.1328] [[file:./.ob-jupyter/6189056a16dfbee33f2c23602d637e78112c1c7d.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mW12_lat24x24_b6.0.png :END: ***** Mass Ratios #+begin_src jupyter-julia :session h2ps bet = 6.0; et1=0.01; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 ttl = latexstring("\\beta=$(bet);~k_1=k_2") ylbl = latexstring("m/m") ks = Vector{Float64}() rr = newsims for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(ks,rsp.kap[1]) end end mHmH = Vector{uwreal}(undef, length(ks)) mHmW = Array{uwreal,2}(undef, 4, length(ks)) mWmW = Vector{uwreal}(undef, length(ks)) plt = plot(reuse=false, xlabel=L"k",ylabel=ylbl, title=ttl,) rr = newsims for i in 1:length(ks) k=ks[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.kap[1] == k && rsp.kap[2]==k mHmH[i] = r.mH[1]/r.mH[2] uwerr(mHmH[i]) mHmW[1,i] = r.mH[1]/r.mW[1] mHmW[2,i] = r.mH[2]/r.mW[2] mHmW[3,i] = r.mH[1]/r.mW[2] mHmW[4,i] = r.mH[2]/r.mW[1] uwerr.(mHmW[:,i]) mWmW[i] = r.mW[1]/r.mW[2] uwerr(mWmW[i]) end end end p=scatter!(ks, value.(mHmH[:]), yerr=err.(mHmH[:]), label=L"m_{H_1}/m_{H_2}",xlims=(0.1319,0.1331)) # p=scatter!(ks, value.(mWmW[:]), yerr=err.(mWmW[:]), label=L"m_{W_1}/m_{W_2}",xlims=(0.1319,0.1331)) r=1 # p=scatter!(ks, value.(mHmW[r,:]), yerr=err.(mHmW[r,:]), label=L"m_{H}/m_{W}",xlims=(0.1319,0.1331)) display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1mH2_lat$(lat)x$(tim)_b$(bet).png" # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mW1_lat$(lat)x$(tim)_b$(bet).png" # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mW1_mW2_lat$(lat)x$(tim)_b$(bet).png" # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mW12_lat$(lat)x$(tim)_b$(bet).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/6c23082dc1d9657636e6de3e881fbf9cc2c4a7fb.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1mH2_lat16x24_b6.0.png :END: ***** Global Obs. #+begin_src jupyter-julia :session h2ps bet = 6.0; et1=0.01; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 ttl = latexstring("\\beta=$(bet);~k_1=k_2") ylbl = latexstring("am") ks = Vector{Float64}() rr = sims for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(ks,rsp.kap[1]) end end sort!(ks) #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 glb = Array{uwreal,2}(undef, 7, length(ks)) mHs = Array{uwreal,2}(undef, 2, length(ks)) plt = plot(reuse=false, xlabel=L"k",ylabel=ylbl, title=ttl,) for i in 1:length(ks) k=ks[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.kap[1] == k && rsp.kap[2]==k glb[:,i] = r.glb uwerr.(glb[:,i]) mHs[:,i] = r.mH uwerr.(mHs[:,i]) end end end st=1 fc=0 g = 6 p=scatter!(ks[st:end-fc], value.(glb[g,st:end-fc]), yerr=err.(glb[g,st:end-fc]), label=L"glb", markershape=:utriangle)#,xlims=(0.1319,0.1331)) print(ks[st:end-fc]) display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mH2_lat$(lat)x$(tim)_b$(bet).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: : [0.132, 0.1322, 0.13222, 0.13223, 0.13224, 0.13225, 0.1323, 0.13235, 0.1324, 0.1325, 0.1326, 0.1328] [[file:./.ob-jupyter/3b159721e9383525ae100610eb3559389357d689.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mH2_lat24x24_b6.0.png :END: **** Scan $k_2$ - ($k_1$ fixed) ***** Mass & Mass ratios #+begin_src jupyter-julia :session h2ps # bet = 5.5; k1=0.133; et1=0.003; et2=0.001; mu=0.0; xi1=0.0001; xi2=0.0001; xi3=0.0; xi4=0.0 bet = 6.0; k1=0.133; et1=0.003; et2=0.001; mu=0.001; xi1=0.0001; xi2=0.0001; xi3=0.0005; xi4=0.0001 ttl = latexstring("\\beta=$(bet);~k_1=$(k1)") ylbl = "" k2vec = Vector{Float64}() rr = sims for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.muh==mu && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(k2vec,rsp.kap[2]) end end sort!(k2vec) #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 glb = Array{uwreal,2}(undef, 7, length(k2vec)) mHs = Array{uwreal,2}(undef, 2, length(k2vec)) mWs = Array{uwreal,2}(undef, 3, length(k2vec)) mHmW = Array{uwreal,2}(undef, 4, length(k2vec)) plt = plot(reuse=false, xlabel=L"\kappa_2",ylabel=ylbl, title=ttl) for i in 1:length(k2vec) k2=k2vec[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.muh==mu && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.kap[2] == k2 glb[:,i] = r.glb uwerr.(glb[:,i]) mHs[:,i] = r.mH uwerr.(mHs[:,i]) mWs[:,i] = r.mW uwerr.(mWs[:,i]) mHmW[1,i] = r.mH[1]/r.mW[1] mHmW[2,i] = r.mH[2]/r.mW[2] mHmW[3,i] = r.mH[1]/r.mW[2] mHmW[4,i] = r.mH[2]/r.mW[1] uwerr.(mHmW[:,i]) end end end st=1 fc=0 print(k2vec) g = 6 # vspan!([k2vec[st], 0.1302], linecolor = :black, fillcolor = :red1,label="",alpha=0.1) # vspan!([0.1302, k2vec[end-fc]], linecolor = :grey, fillcolor = :aqua,label="",alpha=0.1) # p=scatter!([(0.128, 1.6), (0.131, 1.6)]; texts=["(C)","(D)"],label="",markersize=0) #glb # p=scatter!(k2vec[st:end-fc], value.(glb[g,st:end-fc]), yerr=err.(glb[g,st:end-fc]), label=L"L_{\alpha,1}", markershape=:utriangle,legend=:bottomright)#,xlims=(0.1319,0.1331)) # p=scatter!(k2vec[st:end-fc], value.(glb[g+1,st:end-fc]), yerr=err.(glb[g+1,st:end-fc]), label=L"L_{\alpha,2}")#,xlims=(0.1319,0.1331)) #masses # p=scatter!(k2vec[st:end-fc], value.(mHs[1,st:end-fc]), yerr=err.(mHs[1,st:end-fc]), label=L"m_{H_1}", markershape=:utriangle)#,xlims=(0.1319,0.1331)) # p=scatter!(k2vec[st:end-fc], value.(mHs[2,st:end-fc]), yerr=err.(mHs[2,st:end-fc]), label=L"m_{H_2}") p=scatter!(k2vec[st:end-fc], value.(mWs[1,st:end-fc]), yerr=err.(mWs[1,st:end-fc]), label=L"\textrm{Vector}-\Phi_1", markershape=:diamond,markercolor=:red,markerstrokecolor = :red) p=scatter!(k2vec[st:end-fc], value.(mWs[2,st:end-fc]), yerr=err.(mWs[2,st:end-fc]), label=L"\textrm{Vector}-\Phi_2", markershape=:utriangle,markercolor=:black,markerstrokecolor = :black) p=scatter!(k2vec[st:end-fc], value.(mWs[3,st:end-fc]), yerr=err.(mWs[3,st:end-fc]), label=L"\textrm{Vector}-\Phi_1\Phi_2",markercolor=:green,markerstrokecolor = :green) #mass ratios # p=scatter!(k2vec[st:end-fc], value.(mHmW[1,st:end-fc]), yerr=err.(mHmW[1,st:end-fc]), label=L"m_{H_1}/m_{W_1}", markershape=:square,markercolor=:black,markerstrokecolor=:black) # p=scatter!(k2vec[st:end-fc], value.(mHmW[2,st:end-fc]), yerr=err.(mHmW[2,st:end-fc]), label=L"m_{H_2}/m_{W_2}", markershape=:circle) # p=scatter!(k2vec[st:end-fc], value.(mHmW[3,st:end-fc]), yerr=err.(mHmW[3,st:end-fc]), label=L"m_{H_1}/m_{W_2}", markershape=:utriangle) # p=scatter!(k2vec[st:end-fc], value.(mHmW[4,st:end-fc]), yerr=err.(mHmW[4,st:end-fc]), label=L"m_{H_2}/m_{W_1}", markershape=:diamond,legend=:topleft) display(p) # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/presentations/ncts_2023/ps_Lalp12_lat$(lat)x$(tim)_b$(bet).pdf" outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/presentations/ncts_2023/ps_mW1_mW2_mW12_lat$(lat)x$(tim)_b$(bet).pdf" # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/presentations/ncts_2023/ps_mH1_mW1_lat$(lat)x$(tim)_b$(bet).pdf" outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/presentations/ncts_2023/ps_mH1omW1_lat$(lat)x$(tim)_b$(bet).pdf" # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/presentations/ncts_2023/ps_mH1_mH2_lat$(lat)x$(tim)_b$(bet).pdf" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: : [0.1265, 0.127, 0.1275, 0.1285, 0.1287, 0.1288, 0.129, 0.1293] [[file:./.ob-jupyter/39ca44cefccd9111e1ffca84409f718ef674a129.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/presentations/ncts_2023/ps_mH1omW1_lat24x24_b6.0.pdf :END: **** $\eta_2$ plots ***** Mass & Mass ratios #+begin_src jupyter-julia :session h2ps bet = 6.0; k1=0.1324; k2=k1; et1=0.01; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 ttl = latexstring("\\beta=$(bet);~k_1=k_2") ylbl = latexstring("am") et2vec = Vector{Float64}() rr = newsims for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(et2vec,rsp.eta[2]) end end sort!(et2vec) #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 glb = Array{uwreal,2}(undef, 7, length(et2vec)) mHs = Array{uwreal,2}(undef, 2, length(et2vec)) mWs = Array{uwreal,2}(undef, 3, length(et2vec)) mHmW = Array{uwreal,2}(undef, 4, length(et2vec)) plt = plot(reuse=false, xlabel=L"\eta_2",ylabel=ylbl, title=ttl,) for i in 1:length(et2vec) et2=et2vec[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.eta[2] == et2 glb[:,i] = r.glb uwerr.(glb[:,i]) mHs[:,i] = r.mH uwerr.(mHs[:,i]) mWs[:,i] = r.mW uwerr.(mWs[:,i]) mHmW[1,i] = r.mH[1]/r.mW[1] mHmW[2,i] = r.mH[2]/r.mW[2] mHmW[3,i] = r.mH[1]/r.mW[2] mHmW[4,i] = r.mH[2]/r.mW[1] uwerr.(mHmW[:,i]) end end end st=1 fc=1 print(et2vec) g = 6 p=scatter!(et2vec[st:end-fc], value.(glb[g,st:end-fc]), yerr=err.(glb[g,st:end-fc]), label=L"glb", markershape=:utriangle)#,xlims=(0.1319,0.1331)) # p=scatter!(et2vec[st:end-fc], value.(mHs[1,st:end-fc]), yerr=err.(mHs[1,st:end-fc]), label=L"m_{H_1}", markershape=:utriangle)#,xlims=(0.1319,0.1331)) # p=scatter!(et2vec[st:end-fc], value.(mHs[2,st:end-fc]), yerr=err.(mHs[2,st:end-fc]), label=L"m_{H_2}") # p=scatter!(et2vec[st:end-fc], value.(mWs[1,st:end-fc]), yerr=err.(mWs[1,st:end-fc]), label=L"m_{W_1}", markershape=:diamond) # p=scatter!(et2vec[st:end-fc], value.(mWs[2,st:end-fc]), yerr=err.(mWs[2,st:end-fc]), label=L"m_{W_2}") #mass ratios p=scatter!(et2vec[st:end-fc], value.(mHmW[1,st:end-fc]), yerr=err.(mHmW[1,st:end-fc]), label=L"m_{H_1}/m_{W_1}", markershape=:square) # p=scatter!(et2vec[st:end-fc], value.(mHmW[2,st:end-fc]), yerr=err.(mHmW[2,st:end-fc]), label=L"m_{H_2}/m_{W_2}", markershape=:circle) # p=scatter!(et2vec[st:end-fc], value.(mHmW[3,st:end-fc]), yerr=err.(mHmW[3,st:end-fc]), label=L"m_{H_1}/m_{W_2}", markershape=:utriangle) # p=scatter!(et2vec[st:end-fc], value.(mHmW[4,st:end-fc]), yerr=err.(mHmW[4,st:end-fc]), label=L"m_{H_2}/m_{W_1}", markershape=:diamond,legend=:topleft) display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mH2_lat$(lat)x$(tim)_b$(bet).png" print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: : [0.03, 0.04, 0.05, 0.055] [[file:./.ob-jupyter/02df85d60cff472187942ad70df08b9960e8f28d.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mH2_lat16x24_b6.0.png :END: **** $\eta_1$ plots ***** Mass & Mass ratios #+begin_src jupyter-julia :session h2ps bet = 6.0; k1=0.1324; k2=k1; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 ttl = latexstring("\\beta=$(bet);~k_1=k_2") ylbl = "" et1vec = Vector{Float64}() rr = runs for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(et1vec,rsp.eta[1]) end end sort!(et1vec) #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 glb = Array{uwreal,2}(undef, 7, length(et1vec)) mHs = Array{uwreal,2}(undef, 2, length(et1vec)) mWs = Array{uwreal,2}(undef, 3, length(et1vec)) mHmW = Array{uwreal,2}(undef, 4, length(et1vec)) plt = plot(reuse=false, xlabel=L"\eta_1",ylabel=ylbl, title=ttl,) for i in 1:length(et1vec) et1=et1vec[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.eta[1] == et1 glb[:,i] = r.glb uwerr.(glb[:,i]) mHs[:,i] = r.mH uwerr.(mHs[:,i]) mWs[:,i] = r.mW uwerr.(mWs[:,i]) mHmW[1,i] = r.mH[1]/r.mW[1] mHmW[2,i] = r.mH[2]/r.mW[2] mHmW[3,i] = r.mH[1]/r.mW[2] mHmW[4,i] = r.mH[2]/r.mW[1] uwerr.(mHmW[:,i]) end end end st=2 fc=1 print(et1vec) g = 7 # p=scatter!(et1vec[st:end-fc], value.(glb[g,st:end-fc]), yerr=err.(glb[g,st:end-fc]), label=L"glb", markershape=:utriangle)#,xlims=(0.1319,0.1331)) p=scatter!(et1vec[st:end-fc], value.(mHs[1,st:end-fc]), yerr=err.(mHs[1,st:end-fc]), label=L"m_{H_1}", markershape=:utriangle)#,xlims=(0.1319,0.1331)) p=scatter!(et1vec[st:end-fc], value.(mHs[2,st:end-fc]), yerr=err.(mHs[2,st:end-fc]), label=L"m_{H_2}") p=scatter!(et1vec[st:end-fc], value.(mWs[1,st:end-fc]), yerr=err.(mWs[1,st:end-fc]), label=L"m_{W_1}", markershape=:diamond) p=scatter!(et1vec[st:end-fc], value.(mWs[2,st:end-fc]), yerr=err.(mWs[2,st:end-fc]), label=L"m_{W_2}") #mass ratios # p=scatter!(et1vec[st:end-fc], value.(mHmW[1,st:end-fc]), yerr=err.(mHmW[1,st:end-fc]), label=L"m_{H_1}/m_{W_1}", markershape=:square) # p=scatter!(et1vec[st:end-fc], value.(mHmW[2,st:end-fc]), yerr=err.(mHmW[2,st:end-fc]), label=L"m_{H_2}/m_{W_2}", markershape=:circle) # p=scatter!(et1vec[st:end-fc], value.(mHmW[3,st:end-fc]), yerr=err.(mHmW[3,st:end-fc]), label=L"m_{H_1}/m_{W_2}", markershape=:utriangle) # p=scatter!(et1vec[st:end-fc], value.(mHmW[4,st:end-fc]), yerr=err.(mHmW[4,st:end-fc]), label=L"m_{H_2}/m_{W_1}", markershape=:diamond,legend=:topleft) display(p) # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mH2_lat$(lat)x$(tim)_b$(bet).png" # print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: : [0.008, 0.009, 0.01, 0.0102, 0.0104, 0.0106, 0.011, 0.012] [[file:./.ob-jupyter/03aecaf2331c22572718260b21dc7d38ed256d95.svg]] :END: **** $\mu$ plots ***** Mass & Mass ratios #+begin_src jupyter-julia :session h2ps bet = 5.5; k1=0.1324; k2=k1; et1=0.01; et2=0.05; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 ttl = latexstring("\\beta=$(bet);~k_1=k_2") ylbl = "" muvec = Vector{Float64}() rr = sims for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 push!(muvec,rsp.muh) end end sort!(muvec) #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 glb = Array{uwreal,2}(undef, 7, length(muvec)) mHs = Array{uwreal,2}(undef, 2, length(muvec)) mWs = Array{uwreal,2}(undef, 3, length(muvec)) mHmW = Array{uwreal,2}(undef, 4, length(muvec)) plt = plot(reuse=false, xlabel=L"\mu",ylabel=ylbl, title=ttl,) for i in 1:length(muvec) mu=muvec[i] for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && rsp.muh == mu glb[:,i] = r.glb uwerr.(glb[:,i]) mHs[:,i] = r.mH uwerr.(mHs[:,i]) mWs[:,i] = r.mW uwerr.(mWs[:,i]) mHmW[1,i] = r.mH[1]/r.mW[1] mHmW[2,i] = r.mH[2]/r.mW[2] mHmW[3,i] = r.mH[1]/r.mW[2] mHmW[4,i] = r.mH[2]/r.mW[1] uwerr.(mHmW[:,i]) end end end st=1 fc=0 print(muvec) g = 7 # p=scatter!(muvec[st:end-fc], value.(glb[g,st:end-fc]), yerr=err.(glb[g,st:end-fc]), label=L"glb", markershape=:utriangle)#,xlims=(0.1319,0.1331)) # p=scatter!(muvec[st:end-fc], value.(mHs[1,st:end-fc]), yerr=err.(mHs[1,st:end-fc]), label=L"m_{H_1}", markershape=:utriangle)#,xlims=(0.1319,0.1331)) # p=scatter!(muvec[st:end-fc], value.(mHs[2,st:end-fc]), yerr=err.(mHs[2,st:end-fc]), label=L"m_{H_2}") # p=scatter!(muvec[st:end-fc], value.(mWs[1,st:end-fc]), yerr=err.(mWs[1,st:end-fc]), label=L"m_{W_1}", markershape=:diamond) # p=scatter!(muvec[st:end-fc], value.(mWs[2,st:end-fc]), yerr=err.(mWs[2,st:end-fc]), label=L"m_{W_2}") #mass ratios p=scatter!(muvec[st:end-fc], value.(mHmW[1,st:end-fc]), yerr=err.(mHmW[1,st:end-fc]), label=L"m_{H_1}/m_{W_1}", markershape=:square) p=scatter!(muvec[st:end-fc], value.(mHmW[2,st:end-fc]), yerr=err.(mHmW[2,st:end-fc]), label=L"m_{H_2}/m_{W_2}", markershape=:circle) p=scatter!(muvec[st:end-fc], value.(mHmW[3,st:end-fc]), yerr=err.(mHmW[3,st:end-fc]), label=L"m_{H_1}/m_{W_2}", markershape=:utriangle) p=scatter!(muvec[st:end-fc], value.(mHmW[4,st:end-fc]), yerr=err.(mHmW[4,st:end-fc]), label=L"m_{H_2}/m_{W_1}", markershape=:diamond,legend=:topleft) display(p) # outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_mH1_mH2_lat$(lat)x$(tim)_b$(bet).png" # print(outputname) # savefig(outputname) #+end_src #+RESULTS: :RESULTS: : [0.012, 0.014, 0.016, 0.018, 0.02, 0.024] [[file:./.ob-jupyter/1ef0d9d3d2a5d69e40902f89fd618168c045a3e8.svg]] :END: *** Smearing test **** Define struct #+begin_src jupyter-julia :session h2ps mutable struct h2smruns{T} #specific struct for 2HDM simulations gp::GaugeParm{T,SU2{T},0} sp::ScalarParm{2,T} mH::Vector{uwreal} #mH1, mH2 mW::Vector{uwreal} #mW1, mW2, mW12 glb::Vector{uwreal} #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 sus::Int64 sdt::Float64 sss::Int64 srs::Float64 end #+end_src #+RESULTS: #+begin_src jupyter-julia :session h2ps smruns = Vector{h2smruns}() #+end_src #+RESULTS: **** Save measurements #+begin_src jupyter-julia :session h2ps a = h2smruns(gp, sp, mHvec, mWvec, glbvec, sus, sdt, sss, srs) # add run to vector # first check if this beta is already there append = true rr = smruns for i in 1:length(rr) if rr[i].sus == sus && rr[i].sdt == sdt && rr[i].sss == sss && rr[i].srs == srs rr[i] = a append = false end end if append push!(rr, a) print("append") end #+end_src #+RESULTS: **** Plots ***** Table #+begin_src jupyter-julia :session h2ps srsvec = Vector{Float64}() rr = smruns for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && r.sus == sus && r.sdt==sdt if r.srs in srsvec continue else push!(srsvec,r.srs) end end end sort!(sssvec) for sr in srsvec for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && r.sus == sus && r.sdt==sdt && r.srs == sr invmH = 1 ./ (2 .* r.mH .^2 ) uwerr.(invmH) invmW = 1 ./ (2 .* r.mW .^2 ) uwerr.(invmW) Nr = r.srs*r.sss print(r.srs,"&",r.sss," & ",Nr,"&",invmH[1],"&",invmH[2],"&",invmW[1],"&",invmW[2],"\\\\ \n") end end end #+end_src #+RESULTS: #+begin_example 0.05&20 & 1.0&1.9916473030639963 +/- 0.5596566649234749&0.6877201208306659 +/- 0.3570935440439374&0.46874472753028895 +/- 0.14822760705865895&0.22482569852231915 +/- 0.06481581823207333\\ 0.05&40 & 2.0&1.0240083759969287 +/- 0.6463852813505994&0.6470451311196749 +/- 0.260386923138997&0.8031237140936393 +/- 0.16391558967955802&0.3133371109869641 +/- 0.06930460336283135\\ 0.05&60 & 3.0&2.8298731045610444 +/- 1.2053114638154308&0.941062338189833 +/- 0.3549976529059656&1.3701871074665077 +/- 0.4273152336926474&0.6797580159382732 +/- 0.2615353306462885\\ 0.05&80 & 4.0&3.266729242786265 +/- 0.809630545896083&1.3603692985112523 +/- 0.38959739085053813&1.415586975820989 +/- 0.2646962578362908&0.665186932429922 +/- 0.3058478714286901\\ 0.05&100 & 5.0&2.462751068737411 +/- 1.052164286556168&1.1230920876896264 +/- 0.346509144925387&1.6567966966123011 +/- 0.2538180645578262&0.6397020835767105 +/- 0.23354564786661186\\ 0.1&80 & 8.0&3.8998432980121 +/- 0.9298012815223701&2.1608905925853636 +/- 0.8868273200379838&1.57542583806349 +/- 0.17924740363899513&0.8189974242001009 +/- 0.2770073855088487\\ 0.1&60 & 6.0&3.5961634869238104 +/- 1.115711868631567&1.9334547838377563 +/- 1.0140535680777276&3.196841535615 +/- 0.9293884908758987&1.660991571244219 +/- 0.6271102826752062\\ 0.1&100 & 10.0&4.51060626579237 +/- 1.0391523356833294&3.194237395274354 +/- 0.749979295373112&2.8433313346510176 +/- 0.3619817796097922&1.7948102567532578 +/- 0.689087770010343\\ 0.1&40 & 4.0&3.5498194673363797 +/- 1.028103609068106&1.6233849726552545 +/- 0.47909915283233906&1.0935662560074622 +/- 0.5556417601461453&0.6150446995203238 +/- 0.22489116962707614\\ 0.1&20 & 2.0&2.026153467466278 +/- 0.5131520366313931&0.6308445331254703 +/- 0.5097556230686798&1.1110272983818288 +/- 0.2207896900968592&0.39899097599448335 +/- 0.12258537208034473\\ 0.15&20 & 3.0&3.0806214681318065 +/- 0.9772684760737412&1.2449355188755002 +/- 0.5822872019453588&1.206778804312939 +/- 0.27834089066163487&0.5614091980977342 +/- 0.13101234228263917\\ 0.15&40 & 6.0&2.7386246528519487 +/- 0.6730032594241981&1.2832749807521713 +/- 0.42876553057675015&2.096654416069448 +/- 0.4616722575851653&1.0393807043647028 +/- 0.48695356468653783\\ 0.15&60 & 9.0&6.780229596013154 +/- 1.3524727310105498&4.258876234363233 +/- 1.0298811147187266&2.214704890845852 +/- 0.3953995471816991&1.2167780230366239 +/- 0.4309873595596708\\ #+end_example ***** Scalar Smearing Steps -& Scalar $r$ #+begin_src jupyter-julia :session h2ps bet = 5.5; k1=0.1324; k2=k1; et1=0.01; et2=0.05; mu=0.01; xi1=0.005; xi2=0.005; xi3=0.01; xi4=0.01 # sus = 20; sdt = 0.01; srs = 0.05 ttl = latexstring("\\beta=$(bet)") ylbl = "" srsvec = Vector{Float64}() rr = smruns for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && r.sus == sus && r.sdt==sdt if r.srs in srsvec continue else push!(srsvec,r.srs) end end end sort!(sssvec) ds = [1, 0, -1] plt = plot(reuse=false, xlabel="# scalar smearing",ylabel=ylbl, title=ttl,) ct = 0 mname="" for sr in srsvec ct += 1 #plaquette, rho1, rho2, Lphi1, Lphi2, Lalpha1, Lalpha2 mH1 = Vector{uwreal}(); mH2 = Vector{uwreal}() mW1 = Vector{uwreal}(); mW2 = Vector{uwreal}() sssvec = Vector{Int64}() for r in rr rgp = r.gp rsp = r.sp if rgp.beta == bet && rsp.kap[1]==k1 && rsp.kap[2]==k2 && rsp.eta[1] == et1 && rsp.eta[2] == et2 && rsp.muh == mu && rsp.xi[1] == xi1 && rsp.xi[2] == xi2 && rsp.xi[3] == xi3 && rsp.xi[4] == xi4 && r.sus == sus && r.sdt==sdt && r.srs == sr push!(sssvec,r.sss + ds[ct]) push!(mH1, r.mH[1]) push!(mH2, r.mH[2]) push!(mW1, r.mW[1]) push!(mW2, r.mW[2]) uwerr.(mH1); uwerr.(mH2) uwerr.(mW1); uwerr.(mW2) end end st=1 fc=0 # p=scatter!(sssvec[st:end-fc], value.(mH1[st:end-fc]), yerr=err.(mH1[st:end-fc]), label=latexstring("r_{smear}=$(sr)"),title=latexstring("m_{H_1}")); mname = "mH1" # p=scatter!(sssvec[st:end-fc], value.(mH2[st:end-fc]), yerr=err.(mH2[st:end-fc]), label=latexstring("r_{smear}=$(sr)"),title=latexstring("m_{H_2}")); mname = "mH2" # p=scatter!(sssvec[st:end-fc], value.(mW1[st:end-fc]), yerr=err.(mW1[st:end-fc]), label=latexstring("r_{smear}=$(sr)"),title=latexstring("m_{W_1}")); mname = "mW1" # p=scatter!(sssvec[st:end-fc], value.(mW2[st:end-fc]), yerr=err.(mW2[st:end-fc]), label=latexstring("r_{smear}=$(sr)"),title=latexstring("m_{W_2}")); mname = "mW2" end display(p) outputname="/home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_smearing_$(mname)_lat$(lat)x$(tim)_b$(bet).png" print(outputname) savefig(outputname) #+end_src #+RESULTS: :RESULTS: [[file:./.ob-jupyter/a83d17178ddf048d3714307a1af1ca65a0020068.svg]] : /home/gtelo/PhD/LatGPU/SU2proj/code/runs_su2higgs/reports/figs/masses/ps_smearing_mW2_lat16x24_b5.5.png :END: * Copy data h2runs-> h2sim #+begin_src jupyter-julia :session h2ps # sims = Vector{h2sim}() smear = smr{Float64}(20, 0.01, 40, 0.02) dm = (24,24) for r in newsims # a = h2sim(r.gp, r.sp, r.mH, r.mW, r.t0, r.w0, r.flwtime, r.tEpl, r.tEcl, r.glb, dm, smear) # push!(sims,a) # print(r.dim,"\n") end #+end_src #+RESULTS: * Test Bare Phase conditions #+begin_src jupyter-julia :session h2ps #condition C kp1 = 0.132 kp2 = 0.126 eta1 = 0.003 eta2 = 0.001 xi1 = 0.0001 #eta3! xi2 = 0.0001 #eta4! xi3 = 0.0 xi4 = 0.0 print("BOUNDEDNESS:\nη3 > -(η1η2)^1/2\n") print(xi1,"\t\t",-sqrt(eta1*eta2),"\n") print("η3+η4 > -(η1η2)^1/2\n") print(xi1+xi2,"\t\t",-sqrt(eta1*eta2),"\n") print("\nCONDITION C:\n") m1c = 1.0/8.0 - eta1/4.0 l1mu2 = eta1*(1-2.0*eta2-8*kp2) l3mu1 = xi1*(1-2.0*eta1-8*kp1) l34mu1 = (xi1+xi2)*(1-2.0*eta1-8*kp1) print("k > 1/8 - η1/4:\n") print(kp1, "\t\t", m1c,"\n") print("η1(1-2η2-8k2) > η3(1-2η1-8k1)\n") print(l1mu2,"\t\t",l3mu1,"\n") print("η1(1-2η2-8k2) > (η3+η4)(1-2η1-8k1)\n") print(l1mu2,"\t\t",l34mu1,"\n") #+end_src #+RESULTS: #+begin_example BOUNDEDNESS: η3 > -(η1η2)^1/2 0.0001 -0.0017320508075688774 η3+η4 > -(η1η2)^1/2 0.0002 -0.0017320508075688774 CONDITION C: k > 1/8 - η1/4: 0.132 0.12425 η1(1-2η2-8k2) > η3(1-2η1-8k1) -3.0000000000000028e-5 -6.200000000000006e-6 η1(1-2η2-8k2) > (η3+η4)(1-2η1-8k1) -3.0000000000000028e-5 -1.2400000000000012e-5 #+end_example