void rf602_chi2fit()
{
// S e t u p m o d e l
// ---------------------
// Declare observable x
RooRealVar x("x","x",0,10) ;
// Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
RooRealVar mean("mean","mean of gaussians",5) ;
RooRealVar sigma1("sigma1","width of gaussians",0.5) ;
RooRealVar sigma2("sigma2","width of gaussians",1) ;
RooGaussian sig1("sig1","Signal component 1",x,mean,sigma1) ;
RooGaussian sig2("sig2","Signal component 2",x,mean,sigma2) ;
// Build Chebychev polynomial p.d.f.
RooRealVar a0("a0","a0",0.5,0.,1.) ;
RooRealVar a1("a1","a1",0.2,0.,1.) ;
RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
// Sum the signal components into a composite signal p.d.f.
RooRealVar sig1frac("sig1frac","fraction of component 1 in signal",0.8,0.,1.) ;
RooAddPdf sig("sig","Signal",RooArgList(sig1,sig2),sig1frac) ;
// Sum the composite signal and background
RooRealVar bkgfrac("bkgfrac","fraction of background",0.5,0.,1.) ;
RooAddPdf model("model","g1+g2+a",RooArgList(bkg,sig),bkgfrac) ;
// C r e a t e b i n n e d d a t a s e t
// -----------------------------------------
RooDataSet* d = model.generate(x,10000) ;
RooDataHist* dh = d->binnedClone() ;
// Construct a chi^2 of the data and the model.
// When a p.d.f. is used in a chi^2 fit, the probability density scaled
// by the number of events in the dataset to obtain the fit function
// If model is an extended p.d.f, the expected number events is used
// instead of the observed number of events.
model.chi2FitTo(*dh) ;
// NB: It is also possible to fit a RooAbsReal function to a RooDataHist
// using chi2FitTo().
// Note that entries with zero bins are _not_ allowed
// for a proper chi^2 calculation and will give error
// messages
RooDataSet* dsmall = (RooDataSet*) d->reduce(EventRange(1,100)) ;
RooDataHist* dhsmall = dsmall->binnedClone() ;
RooChi2Var chi2_lowstat("chi2_lowstat","chi2",model,*dhsmall) ;
cout << chi2_lowstat.getVal() << endl ;
}
RooDataHist * genHistFromModelPdf(const char * name, RooAbsPdf *model, RooRealVar *var, double ScaleLumi, int range, int rebin, int seed ) {
double genEvents = model->expectedEvents(*var);
TRandom3 *rndm = new TRandom3();
rndm->SetSeed(seed);
double nEvt = rndm->PoissonD( genEvents) ;
int intEvt = ( (nEvt- (int)nEvt) >= 0.5) ? (int)nEvt +1 : int(nEvt);
RooDataSet * data = model->generate(*var , intEvt );
cout<< " expected events for " << name << " = "<< genEvents << endl;
cout<< " data->numEntries() for name " << name << " == " << data->numEntries()<< endl;
// cout<< " nEvt from PoissonD for" << name << " == " << nEvt<< endl;
//cout<< " cast of nEvt for" << name << " == " << intEvt<< endl;
RooAbsData *binned_data = data->binnedClone();
TH1 * toy_hist = binned_data->createHistogram( name, *var, Binning(range/rebin ) );
for(int i = 1; i <= toy_hist->GetNbinsX(); ++i) {
toy_hist->SetBinError( i, sqrt( toy_hist->GetBinContent(i)) );
if(toy_hist->GetBinContent(i) == 0.00) {
cout<< " WARNING: histo " << name << " has 0 enter in bin number " << i << endl;
}
if(toy_hist->GetBinContent(i) < 0.1) {
toy_hist->SetBinContent(i, 0.0);
toy_hist->SetBinError(i, 0.0);
cout<< " WARNING: setting value 0.0 to histo " << name << " for bin number " << i << endl;
}
}
RooDataHist * toy_rooHist = new RooDataHist(name, name , RooArgList(*var), toy_hist );
return toy_rooHist;
}
void runUPWW() {
int higgsMass=125;
double intLumi=5.1;
int nToys = 10;
bool draw=true;
using namespace RooFit;
gROOT->ProcessLine(".L ~/tdrstyle.C");
setTDRStyle();
gStyle->SetPadLeftMargin(0.16);
gROOT->ForceStyle();
gROOT->ProcessLine(".L statsFactory.cc+");
//
// set up test kind
//
double sigRate;
double bkgRate;
if(higgsMass==125){
sigRate = 7.;
bkgRate = 66.;
}else{
cout << "HMMMM.... I don't know that mass point...BYE!" << endl;
return;
}
RooRealVar* mll = new RooRealVar("mll","dilepton mass [GeV]", 12, 80.);
mll->setBins(17);
RooArgSet* obs = new RooArgSet(*mll) ;
// read signal hypothesis 1
TChain *tsigHyp1 = new TChain("angles");
tsigHyp1->Add(Form("datafiles/bdtpresel/%i/SMHiggsWW_%i_JHU.root",higgsMass, higgsMass));
RooDataSet *sigHyp1Data = new RooDataSet("sigHyp1Data","sigHyp1Data",tsigHyp1,*obs);
RooDataHist *sigHyp1Hist = sigHyp1Data->binnedClone(0);
RooHistPdf* sigHyp1Pdf = new RooHistPdf("sigHyp1Pdf", "sigHyp1Pdf", *obs, *sigHyp1Hist);
// read background
TChain *bkgTree = new TChain("angles");
bkgTree->Add(Form("datafiles/bdtpresel/%i/WW_madgraph_8TeV.root",higgsMass));
RooDataSet *bkgData = new RooDataSet("bkgData","bkgData",bkgTree,*obs);
RooDataHist *bkgHist = bkgData->binnedClone(0);
RooHistPdf* bkgPdf = new RooHistPdf("bkgPdf", "bkgPdf", *obs, *bkgHist);
char statResults[25];
statsFactory *hwwuls;
sprintf(statResults,"uls_hww125_%.0ffb.root", intLumi);
hwwuls = new statsFactory(obs, sigHyp1Pdf, sigHyp1Pdf, statResults);
hwwuls->runUpperLimitWithBackground(sigRate*intLumi, bkgRate*intLumi, bkgPdf, nToys);
delete hwwuls;
// draw plots
if(draw) {
RooPlot* plot1 = mll->frame();
TString plot1Name = "mll";
TCanvas* c1 = new TCanvas("c1","c1",400,400);
bkgData->plotOn(plot1,MarkerColor(kBlack));
bkgPdf->plotOn(plot1, LineColor(kBlack), LineStyle(kDashed));
sigHyp1Data->plotOn(plot1,MarkerColor(kRed));
sigHyp1Pdf->plotOn(plot1,LineColor(kRed), LineStyle(kDashed));
// draw...
plot1->Draw();
c1->SaveAs(Form("plots/ul/epsfiles/%s.eps", plot1Name.Data()));
c1->SaveAs(Form("plots/ul/pngfiles/%s.png", plot1Name.Data()));
delete c1;
}
}
void rf307_fullpereventerrors()
{
// B - p h y s i c s p d f w i t h p e r - e v e n t G a u s s i a n r e s o l u t i o n
// ----------------------------------------------------------------------------------------------
// Observables
RooRealVar dt("dt","dt",-10,10) ;
RooRealVar dterr("dterr","per-event error on dt",0.01,10) ;
// Build a gaussian resolution model scaled by the per-event error = gauss(dt,bias,sigma*dterr)
RooRealVar bias("bias","bias",0,-10,10) ;
RooRealVar sigma("sigma","per-event error scale factor",1,0.1,10) ;
RooGaussModel gm("gm1","gauss model scaled bt per-event error",dt,bias,sigma,dterr) ;
// Construct decay(dt) (x) gauss1(dt|dterr)
RooRealVar tau("tau","tau",1.548) ;
RooDecay decay_gm("decay_gm","decay",dt,tau,gm,RooDecay::DoubleSided) ;
// C o n s t r u c t e m p i r i c a l p d f f o r p e r - e v e n t e r r o r
// -----------------------------------------------------------------
// Use landau p.d.f to get empirical distribution with long tail
RooLandau pdfDtErr("pdfDtErr","pdfDtErr",dterr,RooConst(1),RooConst(0.25)) ;
RooDataSet* expDataDterr = pdfDtErr.generate(dterr,10000) ;
// Construct a histogram pdf to describe the shape of the dtErr distribution
RooDataHist* expHistDterr = expDataDterr->binnedClone() ;
RooHistPdf pdfErr("pdfErr","pdfErr",dterr,*expHistDterr) ;
// C o n s t r u c t c o n d i t i o n a l p r o d u c t d e c a y _ d m ( d t | d t e r r ) * p d f ( d t e r r )
// ----------------------------------------------------------------------------------------------------------------------
// Construct production of conditional decay_dm(dt|dterr) with empirical pdfErr(dterr)
RooProdPdf model("model","model",pdfErr,Conditional(decay_gm,dt)) ;
// (Alternatively you could also use the landau shape pdfDtErr)
//RooProdPdf model("model","model",pdfDtErr,Conditional(decay_gm,dt)) ;
// S a m p l e, f i t a n d p l o t p r o d u c t m o d e l
// ------------------------------------------------------------------
// Specify external dataset with dterr values to use model_dm as conditional p.d.f.
RooDataSet* data = model.generate(RooArgSet(dt,dterr),10000) ;
// F i t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
// ---------------------------------------------------------------------
// Specify dterr as conditional observable
model.fitTo(*data) ;
// P l o t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
// ---------------------------------------------------------------------
// Make two-dimensional plot of conditional p.d.f in (dt,dterr)
TH1* hh_model = model.createHistogram("hh_model",dt,Binning(50),YVar(dterr,Binning(50))) ;
hh_model->SetLineColor(kBlue) ;
// Make projection of data an dt
RooPlot* frame = dt.frame(Title("Projection of model(dt|dterr) on dt")) ;
data->plotOn(frame) ;
model.plotOn(frame) ;
// Draw all frames on canvas
TCanvas* c = new TCanvas("rf307_fullpereventerrors","rf307_fullperventerrors",800, 400);
c->Divide(2) ;
c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
}
void runSigSepWWSingle(int higgsMass, double intLumi, int nToys, const TestType test, int var, int toy, bool draw, const unsigned int seed) {
// location of data
// for ucsd batch submission
const char *dataLocation = "root://xrootd.unl.edu//store/user/yygao/HWWAngular/datafiles/";
// for local tests
// const char *dataLocation = "datafiles/";
//
// set up test kind
//
TString testName = getTestName(test);
TString varName = getVarName(var);
TString toyName = getToyName(toy);
std::cout << "Doing " << toyName << " studies on " << testName << " separation based on " << varName << "\n";
double lowMt(0.);
double highMt = higgsMass;
double sigRate;
double bkgRate;
if(higgsMass==125){
sigRate = 13.0;
bkgRate = 155.;
}else{
cout << "HMMMM.... I don't know that mass point...BYE!" << endl;
return;
}
RooRealVar* dphill = new RooRealVar("dphill","#Delta#phi(leptons) [radian]", 0, TMath::Pi());
dphill->setBins(20);
RooRealVar* mt = new RooRealVar("mt","transverse higgs mass", 60, 130);
mt->setBins(10);
RooRealVar* mll = new RooRealVar("mll","dilepton mass [GeV]", 10, 90.);
mll->setBins(10);
RooArgSet* obs;
if ( var == DPHI )
obs = new RooArgSet(*dphill) ;
if ( var == MLL )
obs = new RooArgSet(*mll) ;
if ( var == MLLMT )
obs = new RooArgSet(*mll, *mt) ;
if ( var == DPHIMT )
obs = new RooArgSet(*dphill, *mt) ;
//
// read signal hypothesis 1 always SMHiggs
//
TChain *tsigHyp1 = new TChain("angles");
tsigHyp1->Add(Form("%s/%i/SMHiggsWW_%i_JHU.root", dataLocation, higgsMass, higgsMass));
RooDataSet *sigHyp1Data = new RooDataSet("sigHyp1Data","sigHyp1Data",tsigHyp1,*obs);
RooDataHist *sigHyp1Hist = sigHyp1Data->binnedClone(0);
RooHistPdf* sigHyp1Pdf = new RooHistPdf("sigHyp1Pdf", "sigHyp1Pdf", *obs, *sigHyp1Hist);
// read signal hypothesis 2
TChain *tsigHyp2 = new TChain("angles");
TString secondhypName = getSecondHypInputName(test, float(higgsMass));
tsigHyp2->Add(Form("%s/%i/%s",dataLocation, higgsMass, secondhypName.Data()));
std::cout << secondhypName << "\n";
RooDataSet *sigHyp2Data = new RooDataSet("sigHyp2Data","sigHyp2Data",tsigHyp2,*obs);
RooDataHist *sigHyp2Hist = sigHyp2Data->binnedClone(0);
RooHistPdf* sigHyp2Pdf = new RooHistPdf("sigHyp2Pdf", "sigHyp2Pdf", *obs, *sigHyp2Hist);
// read background
TChain *bkgTree = new TChain("angles");
bkgTree->Add(Form("%s/%i/WW_madgraph_8TeV_0j.root",dataLocation,higgsMass));
RooDataSet *bkgData = new RooDataSet("bkgData","bkgData",bkgTree,*obs);
RooDataHist *bkgHist = bkgData->binnedClone(0);
RooHistPdf* bkgPdf = new RooHistPdf("bkgPdf", "bkgPdf", *obs, *bkgHist);
char statResults[50];
statsFactory *myHypothesisSeparation;
sprintf(statResults,"stat_%s_%s_%s_%.0ffb_%u.root",testName.Data(), toyName.Data(), varName.Data(), intLumi, seed);
printf(statResults);
myHypothesisSeparation = new statsFactory(obs, sigHyp1Pdf, sigHyp2Pdf, seed, statResults);
// running pure toys
myHypothesisSeparation->hypothesisSeparationWithBackground(sigRate*intLumi,sigRate*intLumi,nToys,bkgPdf,bkgRate*intLumi);
delete myHypothesisSeparation;
std::cout << "deleted myHypothesisSeparation" << std::endl;
// draw plots
if(draw) {
RooPlot* plot1;
TString plot1Name;
TCanvas* c1 = new TCanvas("c1","c1",400,400);
if ( var == DPHIMT || var == DPHI) {
plot1 = dphill->frame();
plot1Name = Form("MELAproj_%s_%s_%s_dphi", testName.Data(), toyName.Data(), varName.Data());
}
if ( var == MLL || var == MLLMT) {
plot1 = mll->frame();
plot1Name = Form("MELAproj_%s_%s_%s_mll", testName.Data(), toyName.Data(), varName.Data());
}
bkgData->plotOn(plot1,MarkerColor(kBlack));
bkgPdf->plotOn(plot1, LineColor(kBlack), LineStyle(kDashed));
sigHyp1Data->plotOn(plot1,MarkerColor(kRed));
sigHyp1Pdf->plotOn(plot1,LineColor(kRed), LineStyle(kDashed));
sigHyp2Data->plotOn(plot1,MarkerColor(kBlue));
sigHyp2Pdf->plotOn(plot1,LineColor(kBlue), LineStyle(kDashed));
// draw...
plot1->Draw();
c1->SaveAs(Form("plots/epsfiles/%s.eps", plot1Name.Data()));
c1->SaveAs(Form("plots/pngfiles/%s.png", plot1Name.Data()));
if ( var == DPHIMT || var == MLLMT ) {
RooPlot* plot2 = mt->frame();
TString plot2Name;
plot2Name = Form("MELAproj_%s_%s_%s_mt", testName.Data(), toyName.Data(), varName.Data());
bkgData->plotOn(plot2,MarkerColor(kBlack));
bkgPdf->plotOn(plot2, LineColor(kBlack), LineStyle(kDashed));
sigHyp1Data->plotOn(plot2,MarkerColor(kRed));
sigHyp1Pdf->plotOn(plot2,LineColor(kRed), LineStyle(kDashed));
sigHyp2Data->plotOn(plot2,MarkerColor(kBlue));
sigHyp2Pdf->plotOn(plot2,LineColor(kBlue), LineStyle(kDashed));
c1->Clear();
plot2->Draw();
c1->SaveAs(Form("plots/epsfiles/%s.eps", plot2Name.Data()));
c1->SaveAs(Form("plots/pngfiles/%s.png", plot2Name.Data()));
}
delete c1;
delete plot1;
delete plot2;
}
// tidy up
delete dphill;
delete mt;
delete mll;
delete obs;
delete tsigHyp1;
delete sigHyp1Data;
delete sigHyp1Pdf;
delete tsigHyp2;
delete sigHyp2Data;
delete sigHyp2Pdf;
delete bkgTree;
delete bkgData;
delete bkgPdf;
delete myHypothesisSeparation;
}