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task_6_4.C
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#include "common.h"
void build_gaussian_constraint(RooWorkspace* wspace, TString varname, double mean, double sigma)
{
double min = mean-sigma*6.;
double max = mean+sigma*6.;
if (min<0.) min = 0.;
RooRealVar var(varname, "", mean, min, max);
RooRealVar var_mean(Form("%s_mean",varname.Data()),"",mean);
RooRealVar var_sigma(Form("%s_sigma",varname.Data()),"",sigma);
RooGaussian gau(Form("%s_gau",varname.Data()), "", var, var_mean, var_sigma);
wspace->import(gau);
}
void build_lognormal_constraint(RooWorkspace* wspace, TString varname, double mean, double sigma)
{
double min = mean-sigma*6.;
double max = mean+sigma*6.;
if (min<0.) min = 0.;
RooRealVar var(varname, "", mean, min, max);
RooRealVar var_mean(Form("%s_mean",varname.Data()),"",mean);
RooRealVar var_kappa(Form("%s_kappa",varname.Data()),"",1.+sigma/mean);
RooLognormal lnn(Form("%s_lnn",varname.Data()), "", var, var_mean, var_kappa);
wspace->import(lnn);
}
void build_pdf_peak(RooWorkspace *wspace, uint cate, double bdt_min)
{
vector<TString> decay;
vector<double> yield, yield_err;
// B0 -> mu mu
decay.push_back("bdmmMc");
yield.push_back(effyield[cate][N_bdmmMc]);
yield_err.push_back(effyield[cate][dN_bdmmMc]);
// Bs -> hadron hadron (Bs-> kk ; Bs-> k pi; Bs-> pi pi)
decay.push_back("bstohhMcBg");
yield.push_back(effyield[cate][N_bstohhMcBg]);
yield_err.push_back(effyield[cate][dN_bstohhMcBg]);
// B -> hadron hadron (B-> kk ; B-> k pi; B-> pi pi)
decay.push_back("bdtohhMcBg");
yield.push_back(effyield[cate][N_bdtohhMcBg]);
yield_err.push_back(effyield[cate][dN_bdtohhMcBg]);
RooRealVar m("m","",4.9,5.9);
RooRealVar wgt("wgt","",1.,0.,1000.);
RooDataSet *rds = new RooDataSet("rds","",RooArgSet(m,wgt),"wgt");
double sum_weight = 0.;
double sum_weight_err = 0.;
double weight_max = 0.; // get the largest weight value across samples
for(int proc=0; proc<(int)decay.size(); proc++) {
TFile *fin = new TFile("/eos/uscms/store/user/cmsdas/2025/long_exercises/long-ex-bs-mumu/" + decay[proc]+".root");
TTree *tin = (TTree*)fin->Get(decay[proc]);
double weight = yield[proc]/(double)tin->GetEntries(Form("cate==%d",cate));
if (weight>weight_max) weight_max = weight;
delete fin;
}
for(int proc=0; proc<(int)decay.size(); proc++) {
TFile *fin = new TFile("/eos/uscms/store/user/cmsdas/2025/long_exercises/long-ex-bs-mumu/" + decay[proc]+".root");
TTree *tin = (TTree*)fin->Get(decay[proc]);
unsigned int cate_t;
float m_t,bdt_t;
tin->SetBranchAddress("cate",&cate_t);
tin->SetBranchAddress("m",&m_t);
tin->SetBranchAddress("bdt",&bdt_t);
double weight = yield[proc]/(double)tin->GetEntries(Form("cate==%d",cate));
double weight_err = yield_err[proc]/(double)tin->GetEntries(Form("cate==%d",cate))/weight_max;
std::cout<<"weight "<<weight<<" weight_err"<<weight_err<<std::endl;
for(int evt=0; evt<tin->GetEntries(); evt++) {
tin->GetEntry(evt);
if (cate_t!=cate) continue;
if (bdt_t<=bdt_min) continue;
m.setVal(m_t);
wgt.setVal(weight/weight_max); // rescale the event with largest weight to be 1
rds->add(RooArgSet(m,wgt),weight/weight_max);
sum_weight += weight;
sum_weight_err += weight_err; // systematics; linear sum
}
delete fin;
}
RooKeysPdf pdf(Form("pdf_peak_%d",cate), "", m, *rds, RooKeysPdf::NoMirror, 2.0);
RooPlot *frame = m.frame(Title(" "),Bins(100));
rds->plotOn(frame, Name("t_rds"));
pdf.plotOn(frame, Name("t_pdf"), LineWidth(3));
TCanvas* canvas = new TCanvas("canvas", "", 600, 600);
canvas->SetMargin(0.15,0.06,0.13,0.07);
frame->GetYaxis()->SetTitleOffset(1.50);
frame->GetYaxis()->SetTitle("Entries / 0.01 GeV");
frame->GetXaxis()->SetTitleOffset(1.15);
frame->GetXaxis()->SetLabelOffset(0.01);
frame->GetXaxis()->SetTitle("M(#mu#mu) [GeV]");
frame->GetXaxis()->SetTitleSize(0.043);
frame->GetYaxis()->SetTitleSize(0.043);
frame->Draw();
TLegend* leg = new TLegend(0.58,0.77,0.93,0.92);
leg->SetFillStyle(0);
leg->SetLineWidth(0);
leg->SetHeader(Form("Category %d",cate));
leg->AddEntry(frame->findObject("t_rds"),"Simulation","EP");
leg->AddEntry(frame->findObject("t_pdf"),"PDF","L");
leg->Draw();
canvas->Print(Form("task6_4a_%d.pdf",cate));
cout << "Category: " << cate << endl;
cout << "BDT min: " << bdt_min << endl;
cout << "Sum of weights: " << sum_weight << " +- " << sum_weight_err << endl;
wspace->import(pdf);
build_lognormal_constraint(wspace,Form("N_peak_%d",cate),sum_weight,sum_weight_err);
delete frame;
delete rds;
delete leg;
delete canvas;
}
void build_pdf_semi(RooWorkspace *wspace, uint cate, double bdt_min)
{
vector<TString> decay;
vector<double> yield, yield_err;
decay.push_back("bskmunuMcBg");
yield.push_back(effyield[cate][N_bskmunuMcBg]);
yield_err.push_back(effyield[cate][dN_bskmunuMcBg]);
decay.push_back("bdpimunuMcBg");
yield.push_back(effyield[cate][N_bdpimunuMcBg]);
yield_err.push_back(effyield[cate][dN_bdpimunuMcBg]);
decay.push_back("bdpimumuMcBg");
yield.push_back(effyield[cate][N_bdpimumuMcBg]);
yield_err.push_back(effyield[cate][dN_bdpimumuMcBg]);
decay.push_back("bupimumuMcBg");
yield.push_back(effyield[cate][N_bupimumuMcBg]);
yield_err.push_back(effyield[cate][dN_bupimumuMcBg]);
RooRealVar m("m","",4.9,5.9);
RooRealVar wgt("wgt","",1.,0.,1000.);
RooDataSet *rds = new RooDataSet("rds","",RooArgSet(m,wgt),"wgt");
double sum_weight = 0.;
double sum_weight_err = 0.;
for(int proc=0; proc<(int)decay.size(); proc++) {
TFile *fin = new TFile("/eos/uscms/store/user/cmsdas/2025/long_exercises/long-ex-bs-mumu/"+decay[proc]+".root");
TTree *tin = (TTree*)fin->Get(decay[proc]);
uint cate_t;
float m_t,bdt_t;
tin->SetBranchAddress("cate",&cate_t);
tin->SetBranchAddress("m",&m_t);
tin->SetBranchAddress("bdt",&bdt_t);
double weight = yield[proc]/(double)tin->GetEntries(Form("cate==%d",cate));
double weight_err = yield_err[proc]/(double)tin->GetEntries(Form("cate==%d",cate));
for(int evt=0; evt<tin->GetEntries(); evt++) {
tin->GetEntry(evt);
if (cate_t!=cate) continue;
if (bdt_t<=bdt_min) continue;
m.setVal(m_t);
wgt.setVal(weight);
rds->add(RooArgSet(m,wgt),weight);
sum_weight += weight;
sum_weight_err += weight_err; // systematics; linear sum
}
delete fin;
}
RooKeysPdf pdf(Form("pdf_semi_%d",cate), "", m, *rds, RooKeysPdf::MirrorLeft, 2.0);
RooPlot *frame = m.frame(Title(" "),Bins(50));
rds->plotOn(frame, Name("t_rds"));
pdf.plotOn(frame, Name("t_pdf"), LineWidth(3));
TCanvas* canvas = new TCanvas("canvas", "", 600, 600);
canvas->SetMargin(0.15,0.06,0.13,0.07);
frame->GetYaxis()->SetTitleOffset(1.50);
frame->GetYaxis()->SetTitle("Entries / 0.02 GeV");
frame->GetXaxis()->SetTitleOffset(1.15);
frame->GetXaxis()->SetLabelOffset(0.01);
frame->GetXaxis()->SetTitle("M(#mu#mu) [GeV]");
frame->GetXaxis()->SetTitleSize(0.043);
frame->GetYaxis()->SetTitleSize(0.043);
frame->Draw();
TLegend* leg = new TLegend(0.58,0.77,0.93,0.92);
leg->SetFillStyle(0);
leg->SetLineWidth(0);
leg->SetHeader(Form("Category %d",cate));
leg->AddEntry(frame->findObject("t_rds"),"Simulation","EP");
leg->AddEntry(frame->findObject("t_pdf"),"PDF","L");
leg->Draw();
canvas->Print(Form("task6_4b_%d.pdf",cate));
cout << "Category: " << cate << endl;
cout << "BDT min: " << bdt_min << endl;
cout << "Sum of weights: " << sum_weight << " +- " << sum_weight_err << endl;
wspace->import(pdf);
build_lognormal_constraint(wspace,Form("N_semi_%d",cate),sum_weight,sum_weight_err);
delete frame;
delete rds;
delete leg;
delete canvas;
}
void build_pdf_bs(RooWorkspace *wspace, uint cate, double bdt_min)
{
RooRealVar m("m","",4.9,5.9);
RooDataSet *rds = new RooDataSet("rds","",RooArgSet(m));
TFile *fin = new TFile("/eos/uscms/store/user/cmsdas/2025/long_exercises/long-ex-bs-mumu/bsmmMc.root");
TTree *tin = (TTree*)fin->Get("bsmmMc");
uint cate_t;
float m_t,bdt_t;
tin->SetBranchAddress("cate",&cate_t);
tin->SetBranchAddress("m",&m_t);
tin->SetBranchAddress("bdt",&bdt_t);
int evtcount[2] = {0,0};
for(int evt=0; evt<tin->GetEntries(); evt++) {
tin->GetEntry(evt);
if (cate_t!=cate) continue;
evtcount[0]++;
if (bdt_t<=bdt_min) continue;
evtcount[1]++;
m.setVal(m_t);
rds->add(RooArgSet(m));
}
double eff = (double)evtcount[1]/(double)evtcount[0] * effyield[cate][Eff_bsmmMc];
double eff_error = effyield[cate][dEff_bsmmMc]/effyield[cate][Eff_bsmmMc]*eff; // ignore MC statistics error
delete fin;
RooRealVar bs_mean1(Form("bs_%d_mean1",cate),"",5.37,5.2,5.5);
RooRealVar bs_mean2(Form("bs_%d_mean2",cate),"",5.37,5.2,5.5);
RooRealVar bs_sigma1(Form("bs_%d_sigma1",cate),"",0.030,0.005,0.060);
RooRealVar bs_sigma2(Form("bs_%d_sigma2",cate),"",0.080,0.040,0.200);
RooRealVar bs_cbalpha(Form("bs_%d_cbalpha",cate),"",1.,0.,4.);
RooRealVar bs_cbn(Form("bs_%d_cbn",cate),"",1.,0.,4.);
RooRealVar bs_frac(Form("bs_%d_frac",cate),"",0.7,0.,1.);
RooGaussian bs_gaus(Form("bs_%d_gaus",cate), "", m, bs_mean1, bs_sigma1);
RooCBShape bs_cbline(Form("bs_%d_cbline",cate), "", m, bs_mean2, bs_sigma2, bs_cbalpha, bs_cbn);
RooAddPdf pdf(Form("pdf_bs_%d",cate),"",RooArgList(bs_gaus,bs_cbline),RooArgList(bs_frac));
pdf.fitTo(*rds);
RooPlot *frame = m.frame(Title(" "),Bins(100));
rds->plotOn(frame, Name("t_rds"));
pdf.plotOn(frame, Name("t_pdf"), LineWidth(3));
TCanvas* canvas = new TCanvas("canvas", "", 600, 600);
canvas->SetMargin(0.15,0.06,0.13,0.07);
frame->GetYaxis()->SetTitleOffset(1.50);
frame->GetYaxis()->SetTitle("Entries / 0.01 GeV");
frame->GetXaxis()->SetTitleOffset(1.15);
frame->GetXaxis()->SetLabelOffset(0.01);
frame->GetXaxis()->SetTitle("M(#mu#mu) [GeV]");
frame->GetXaxis()->SetTitleSize(0.043);
frame->GetYaxis()->SetTitleSize(0.043);
frame->Draw();
TLegend* leg = new TLegend(0.58,0.77,0.93,0.92);
leg->SetFillStyle(0);
leg->SetLineWidth(0);
leg->SetHeader(Form("Category %d",cate));
leg->AddEntry(frame->findObject("t_rds"),"Simulation","EP");
leg->AddEntry(frame->findObject("t_pdf"),"PDF","L");
leg->Draw();
canvas->Print(Form("task6_4c_%d.pdf",cate));
cout << "Category: " << cate << endl;
cout << "BDT min: " << bdt_min << endl;
bs_mean1.setConstant(true);
bs_mean2.setConstant(true);
bs_sigma1.setConstant(true);
bs_sigma2.setConstant(true);
bs_cbalpha.setConstant(true);
bs_cbn.setConstant(true);
bs_frac.setConstant(true);
wspace->import(pdf);
build_gaussian_constraint(wspace,Form("Eff_bs_%d",cate),eff,eff_error);
delete frame;
delete rds;
delete leg;
delete canvas;
}
void fit_bupsik(RooWorkspace *wspace, uint cate, double bdt_min)
{
RooRealVar m("m","",5.0,5.8);
RooRealVar wgt("wgt","",1.,0.,1000.);
RooDataSet *rds_data = new RooDataSet("rds_data","",RooArgSet(m,wgt),"wgt");
RooDataSet *rds_mc = new RooDataSet("rds_mc","",RooArgSet(m));
TFile *fin = new TFile("/eos/uscms/store/user/cmsdas/2025/long_exercises/long-ex-bs-mumu/bupsikData.root");
TTree *tin = (TTree*)fin->Get("bupsikData");
uint cate_t;
float wgt_t,m_t;
tin->SetBranchAddress("cate",&cate_t);
tin->SetBranchAddress("wgt",&wgt_t);
tin->SetBranchAddress("m",&m_t);
for(int evt=0; evt<tin->GetEntries(); evt++) {
tin->GetEntry(evt);
if (cate_t!=cate) continue;
if (m_t<5.0 || m_t>=5.8) continue;
m.setVal(m_t);
wgt.setVal(wgt_t);
rds_data->add(RooArgSet(m,wgt),wgt_t);
}
delete fin;
fin = new TFile("/eos/uscms/store/user/cmsdas/2025/long_exercises/long-ex-bs-mumu/bupsikMc.root");
tin = (TTree*)fin->Get("bupsikMc");
tin->SetBranchAddress("cate",&cate_t);
tin->SetBranchAddress("m",&m_t);
int evtcount[2] = {0,0};
for(int evt=0; evt<tin->GetEntries(); evt++) {
tin->GetEntry(evt);
if (cate_t!=cate) continue;
evtcount[0]++;
if (m_t<5.0 || m_t>=5.8) continue;
evtcount[1]++;
m.setVal(m_t);
rds_mc->add(RooArgSet(m));
}
double eff = (double)evtcount[1]/(double)evtcount[0] * effyield[cate][Eff_bupsikMc];
double eff_error = effyield[cate][dEff_bupsikMc]/effyield[cate][Eff_bupsikMc]*eff; // ignore MC statistics error
delete fin;
RooRealVar sigmc_mean1("sigmc_mean1","",5.28,5.2,5.4);
RooRealVar sigmc_mean2("sigmc_mean2","",5.28,5.2,5.4);
RooRealVar sigmc_sigma1("sigmc_sigma1","",0.030,0.005,0.060);
RooRealVar sigmc_sigma2("sigmc_sigma2","",0.080,0.040,0.200);
RooRealVar sig_frac("sig_frac","",0.9,0.5,1.0);
RooGaussian sigmc_g1("sig_g1","",m,sigmc_mean1,sigmc_sigma1);
RooGaussian sigmc_g2("sig_g2","",m,sigmc_mean2,sigmc_sigma2);
RooAddPdf pdf_sigmc("pdf_sigmc","",RooArgList(sigmc_g1,sigmc_g2),RooArgList(sig_frac));
pdf_sigmc.fitTo(*rds_mc);
RooPlot *frame1 = m.frame(Title(" "),Bins(80));
rds_mc->plotOn(frame1, Name("t_rds_mc"));
pdf_sigmc.plotOn(frame1, Name("t_pdf_sigmc"), LineWidth(3));
TCanvas* canvas1 = new TCanvas("canvas1", "", 600, 600);
canvas1->SetMargin(0.15,0.06,0.13,0.07);
frame1->GetYaxis()->SetTitleOffset(1.50);
frame1->GetYaxis()->SetTitle("Entries / 0.01 GeV");
frame1->GetXaxis()->SetTitleOffset(1.15);
frame1->GetXaxis()->SetLabelOffset(0.01);
frame1->GetXaxis()->SetTitle("M(#mu#muK) [GeV]");
frame1->GetXaxis()->SetTitleSize(0.043);
frame1->GetYaxis()->SetTitleSize(0.043);
frame1->Draw();
TLegend* leg1 = new TLegend(0.58,0.77,0.93,0.92);
leg1->SetFillStyle(0);
leg1->SetLineWidth(0);
leg1->SetHeader(Form("Category %d",cate));
leg1->AddEntry(frame1->findObject("t_rds_mc"),"Simulation","EP");
leg1->AddEntry(frame1->findObject("t_pdf_sigmc"),"Fit","L");
leg1->Draw();
canvas1->Print(Form("task6_4d_%d.pdf",cate));
sigmc_mean1.setConstant(true);
sigmc_mean2.setConstant(true);
sigmc_sigma1.setConstant(true);
sigmc_sigma2.setConstant(true);
sig_frac.setConstant(true);
RooRealVar sig_shift("sig_shift","",0.,-0.02,0.02);
RooRealVar sig_scale("sig_scale","",1.,0.8,1.2);
RooAddition sig_mean1("sig_mean1","",RooArgList(sigmc_mean1,sig_shift));
RooAddition sig_mean2("sig_mean2","",RooArgList(sigmc_mean2,sig_shift));
RooProduct sig_sigma1("sig_sigma1","",RooArgList(sigmc_sigma1,sig_scale));
RooProduct sig_sigma2("sig_sigma2","",RooArgList(sigmc_sigma2,sig_scale));
RooGaussian sig_g1("sig_g1","",m,sig_mean1,sig_sigma1);
RooGaussian sig_g2("sig_g2","",m,sig_mean2,sig_sigma2);
RooAddPdf pdf_sig("pdf_sig","",RooArgList(sig_g1,sig_g2),RooArgList(sig_frac));
RooRealVar comb_coeff("comb_coeff","",-1.2,-10.,10.);
RooExponential pdf_comb("pdf_comb","",m,comb_coeff);
RooRealVar jpsix_scale("jpsix_scale","",0.02,0.001,0.08);
RooRealVar jpsix_shift("jpsix_shift","",5.13,5.12,5.16);
RooGenericPdf pdf_jpsix("pdf_jpsix","","TMath::Erfc((@0-@1)/@2)",RooArgList(m,jpsix_shift,jpsix_scale));
double n_comb_guess = rds_data->sumEntries("m>5.4")*2.;
double n_sig_guess = rds_data->sumEntries("m>5.18&&m<5.38")-n_comb_guess/4.;
double n_jpsix_guess = rds_data->sumEntries("m<5.18")-n_comb_guess*0.18/0.8;
RooRealVar n_sig("n_sig","",n_sig_guess,0.,rds_data->sumEntries());
RooRealVar n_comb("n_comb","",n_comb_guess,0.,rds_data->sumEntries());
RooRealVar n_jpsix("n_jpsix","",n_jpsix_guess,0.,rds_data->sumEntries());
RooAddPdf model("model","",RooArgList(pdf_sig,pdf_comb,pdf_jpsix),RooArgList(n_sig,n_comb,n_jpsix));
model.fitTo(*rds_data, SumW2Error(true));
RooPlot *frame2 = m.frame(Title(" "),Bins(80));
rds_data->plotOn(frame2, Name("t_rds_data"));
model.plotOn(frame2, Name("t_model"), LineWidth(3));
model.plotOn(frame2, Name("t_pdf_comb"), Components("pdf_comb"), LineWidth(3), LineStyle(2), LineColor(kGray+1));
TCanvas* canvas2 = new TCanvas("canvas2", "", 600, 600);
canvas2->SetMargin(0.15,0.06,0.13,0.07);
frame2->GetYaxis()->SetTitleOffset(1.50);
frame2->GetYaxis()->SetTitle("Entries / 0.01 GeV");
frame2->GetXaxis()->SetTitleOffset(1.15);
frame2->GetXaxis()->SetLabelOffset(0.01);
frame2->GetXaxis()->SetTitle("M(#mu#muK) [GeV]");
frame2->GetXaxis()->SetTitleSize(0.043);
frame2->GetYaxis()->SetTitleSize(0.043);
frame2->Draw();
TLegend* leg2 = new TLegend(0.58,0.77,0.93,0.92);
leg2->SetFillStyle(0);
leg2->SetLineWidth(0);
leg2->SetHeader(Form("Category %d",cate));
leg2->AddEntry(frame2->findObject("t_rds_data"),"Data","EP");
leg2->AddEntry(frame2->findObject("t_model"),"Fit","L");
leg2->AddEntry(frame2->findObject("t_pdf_comb"),"Combinatorial bkg.","L");
leg2->Draw();
canvas2->Print(Form("task6_4e_%d.pdf",cate));
cout << "Category: " << cate << endl;
cout << "Selection efficiency: " << eff << " +- " << eff_error << endl;
cout << "Observed yield: " << n_sig.getVal() << " +- " << n_sig.getError() << endl;
build_gaussian_constraint(wspace,Form("N_bu_%d",cate),n_sig.getVal(),n_sig.getError());
wspace->import(RooRealVar(Form("Eff_bu_%d",cate),"",eff));
delete frame1;
delete frame2;
delete rds_mc;
delete rds_data;
delete leg1;
delete leg2;
delete canvas1;
delete canvas2;
}
void build_pdf_comb(RooWorkspace *wspace, uint cate, double bdt_min)
{
RooRealVar m("m","",4.9,5.9);
RooRealVar comb_B1(Form("comb_B1_%d",cate), "", 0.5, 0. , 1);
RooFormulaVar comb_B2(Form("comb_B2_%d",cate), "", "1.-@0", RooArgList(comb_B1));
RooBernstein pdf(Form("pdf_comb_%d",cate), "", m, RooArgList(comb_B1, comb_B2));
TFile *fin = new TFile("/eos/uscms/store/user/cmsdas/2025/long_exercises/long-ex-bs-mumu/bmmData-blind.root");
TTree *tin = (TTree*)fin->Get("bmmData");
double N_comb_guess = (double)tin->GetEntries(Form("cate==%d&&bdt>%g&&m>5.45",cate,bdt_min));
N_comb_guess *= 1.0/0.45; // scale to full mass region
delete fin;
cout << "Category: " << cate << endl;
wspace->import(pdf);
wspace->import(RooRealVar(Form("N_comb_%d",cate),"",N_comb_guess,0.,N_comb_guess*10.));
}
void task_6_4()
{
gROOT->SetBatch();
RooWorkspace *wspace = new RooWorkspace("wspace");
for(uint cate=0; cate<N_Categories; cate++) {
double bdt_min = 0.80;
build_pdf_peak(wspace,cate,bdt_min);
build_pdf_semi(wspace,cate,bdt_min);
build_pdf_bs(wspace,cate,bdt_min);
build_pdf_comb(wspace,cate,bdt_min);
fit_bupsik(wspace, cate, bdt_min);
}
// BF(B+ -> J/psi K+) = (1.010 +- 0.028) E-3 (PDG)
// BF(J/psi -> mu+mu-) = (5.961 +- 0.033) E-2 (PDG)
// Total uncertainty is 2.8%
build_gaussian_constraint(wspace, "BF_bu", 1.010E-3*5.961E-2, 1.010E-3*5.961E-2*0.028);
// fs/fu = 0.252 +- 0.012 (PDG) +- 0.015 (energy/pt dependence)
build_gaussian_constraint(wspace, "fs_over_fu", 0.252, sqrt(pow(0.012,2)+pow(0.015,2)));
wspace->writeToFile("wspace_constr.root");
delete wspace;
}