void JetTagBin::generateData()
{
if ( jtData_ ) delete jtData_;
int nToGen = (int) nevt()+.5;
int nToGenPoisson = RooRandom::randomGenerator()->Poisson(nToGen) ;
jtData_ = jtPdf_->generateBinned( RooArgList(*jtBinVar_), nToGenPoisson, Verbose(1));
if ( verbose ) cout << "Generated data histogram with " << jtData_->numEntries() << " bins and "
<< jtData_->sumEntries() << " events" << endl
<< "Using seed: " << RooRandom::randomGenerator()->GetSeed() << endl;
}
void PrepareWorkspace_GaussOverFlat_withSystematics( TString fileName = "WS_GaussOverFlat_withSystematics.root" )
{
// In this macro a PDF model is built assuming signal has a Gaussian
// PDF and the background a flat PDF. The parameter of interest is
// the signal yield and we assume for it a flat prior. It is shown
// how two types of systematics uncertainties can be expressed;
// those are a sytematic uncertainty on the background yield and
// another on one of the parameters (sigma) of the signal shape.
// All needed objects are stored in a ROOT file (within a
// RooWorkspace container); this ROOT file can then be fed as input
// to various statistical methods.
using namespace RooFit;
using namespace RooStats;
// use an observable for this shape-based analysis
RooRealVar* mass = new RooRealVar("mass","mass",0,500,"GeV/c^{2}");
mass->setBins(100);
RooArgSet* observables = new RooArgSet(*mass,"observables");
// signal (Gaussian) and background (flat) PDFs
RooRealVar* sigSigma = new RooRealVar("sigSigma","sigma in signal PDF",0,100);
RooAbsPdf* sigPdf = new RooGaussian("sigPdf","signal PDF",*mass,RooConst(200),*sigSigma);
RooAbsPdf* bkgPdf = new RooPolynomial("bkgPdf","background PDF",*mass,RooFit::RooConst(0));
// S+B model: the sum of both shapes weighted with the yields
RooRealVar* S = new RooRealVar("S","signal yield",10,0,50);
RooRealVar* B = new RooRealVar("B","background yield",10,0,50);
RooAbsPdf* model = new RooAddPdf("model","S+B PDF",RooArgList(*sigPdf,*bkgPdf),RooArgList(*S,*B));
// B-only model: the same as with a signal yield fixed to 0
RooAbsPdf* modelBkg = new RooExtendPdf("modelBkg","B-only PDF",*bkgPdf,*B);
// assume a Gaussian uncertainty on the detector resolution affecting the signal width (of 10%)
// another nuisance parameter is the background yield (apply an uncertainty of 20%)
RooAbsPdf* prior_sigSigma = new RooGaussian("prior_sigSigma","prior probability on sigSigma",*sigSigma,RooConst(sigSigma->getVal()),RooConst(sigSigma->getVal()*0.10));
RooAbsPdf* prior_B = new RooGaussian("prior_B","prior probability on B",*B,RooConst(B->getVal()),RooConst(B->getVal()*0.20));
RooAbsPdf* priorNuisance = new RooProdPdf("priorNuisance","prior on the nuisance parameters",*prior_B,*prior_sigSigma);
RooArgSet* parameters = new RooArgSet(*B,*sigSigma,"parameters");
// assume a flat prior on our parameter of interest (POI) which is the signal yield
RooAbsPdf* priorPOI = new RooPolynomial("priorPOI","flat prior on the POI",*S,RooFit::RooConst(0));
RooArgSet* POI = new RooArgSet(*S,"POI");
// different options are shown for the data generation from the model
// unbinned data with Poisson fluctuations
// RooAbsData* data = (RooDataSet*) model->generate(*observables,RooFit::Extended(),Name("data"));
// binned data with Poisson fluctuations
// RooAbsData* data = (RooDataHist*) model->generateBinned(*observables,Extended(),Name("data"));
// binned without any fluctuations (average case)
RooAbsData* data = (RooDataHist*) model->generateBinned(*observables,Name("data"),ExpectedData());
// control plot of the generated data
// RooPlot* plot = mass->frame();
// data->plotOn(plot);
// plot->Draw();
// use a RooWorkspace to store the pdf models, prior informations, list of parameters,...
RooWorkspace myWS("myWS");
myWS.import(*data,Rename("data"));
myWS.import(*model,RecycleConflictNodes());
myWS.import(*modelBkg,RecycleConflictNodes());
myWS.import(*priorPOI,RecycleConflictNodes());
myWS.import(*priorNuisance,RecycleConflictNodes());
myWS.defineSet("observables",*observables,kTRUE);
myWS.defineSet("parameters",*parameters,kTRUE);
myWS.defineSet("POI",*POI,kTRUE);
// store the workspace in a ROOT file
TFile file(fileName,"RECREATE");
file.cd();
myWS.Write();
file.Write();
file.Close();
std::cout << "\nRooFit model initialized and stored in " << fileName << std::endl;
}
