///
/// Store the errors as RooFit error into the observables
/// to have them easily available for the pull computation.
///
void PDF_Abs::storeErrorsInObs()
{
if ( covMatrix==0 )
{
cout << "PDF_Abs::storeErrorsInObs() : ERROR : covMatrix not initialized." << endl;
return;
}
for ( int i=0; i<nObs; i++ )
{
RooRealVar* pObs = (RooRealVar*)((RooArgList*)observables)->at(i);
pObs->setError(sqrt(covMatrix[i][i]));
}
}
void fitbkgdataCard(TString configCard="template.config",
bool dobands = true, // create baerror bands for BG models
bool dosignal = false, // plot the signal model (needs to be present)
bool blinded = true, // blind the data in the plots?
bool verbose = true ) {
gROOT->Macro("MitStyle.C");
gStyle->SetErrorX(0);
gStyle->SetOptStat(0);
gROOT->ForceStyle();
TString projectDir;
std::vector<TString> catdesc;
std::vector<TString> catnames;
std::vector<int> polorder;
double massmin = -1.;
double massmax = -1.;
double theCMenergy = -1.;
bool readStatus = readFromConfigCard( configCard,
projectDir,
catnames,
catdesc,
polorder,
massmin,
massmax,
theCMenergy
);
if( !readStatus ) {
std::cerr<<" ERROR: Could not read from card > "<<configCard.Data()<<" <."<<std::endl;
return;
}
TFile *fdata = new TFile(TString::Format("%s/CMS-HGG-data.root",projectDir.Data()),"READ");
if( !fdata ) {
std::cerr<<" ERROR: Could not open file "<<projectDir.Data()<<"/CMS-HGG-data.root."<<std::endl;
return;
}
if( !gSystem->cd(TString::Format("%s/databkg/",projectDir.Data())) ) {
std::cerr<<" ERROR: Could not change directory to "<<TString::Format("%s/databkg/",projectDir.Data()).Data()<<"."<<std::endl;
return;
}
// ----------------------------------------------------------------------
// load the input workspace....
RooWorkspace* win = (RooWorkspace*)fdata->Get("cms_hgg_workspace_data");
if( !win ) {
std::cerr<<" ERROR: Could not load workspace > cms_hgg_workspace_data < from file > "<<TString::Format("%s/CMS-HGG-data.root",projectDir.Data()).Data()<<" <."<<std::endl;
return;
}
RooRealVar *intLumi = win->var("IntLumi");
RooRealVar *hmass = win->var("CMS_hgg_mass");
if( !intLumi || !hmass ) {
std::cerr<<" ERROR: Could not load needed variables > IntLumi < or > CMS_hgg_mass < forom input workspace."<<std::endl;
return;
}
//win->Print();
hmass->setRange(massmin,massmax);
hmass->setBins(4*(int)(massmax-massmin));
hmass->SetTitle("m_{#gamma#gamma}");
hmass->setUnit("GeV");
hmass->setRange("fitrange",massmin,massmax);
hmass->setRange("blind1",100.,110.);
hmass->setRange("blind2",150.,180.);
// ----------------------------------------------------------------------
// some auxiliray vectro (don't know the meaning of all of them ... yet...
std::vector<RooAbsData*> data_vec;
std::vector<RooAbsPdf*> pdfShape_vec; // vector to store the NOT-EXTENDED PDFs (aka pdfshape)
std::vector<RooAbsPdf*> pdf_vec; // vector to store the EXTENDED PDFs
std::vector<RooAbsReal*> normu_vec; // this holds the normalization vars for each Cat (needed in bands for combined cat)
RooArgList normList; // list of range-limityed normalizations (needed for error bands on combined category)
//std::vector<RooRealVar*> coeffv;
//std::vector<RooAbsReal*> normu_vecv; // ???
// ----------------------------------------------------------------------
// define output works
RooWorkspace *wOut = new RooWorkspace("wbkg","wbkg") ;
// util;ities for the combined fit
RooCategory finalcat ("finalcat", "finalcat") ;
RooSimultaneous fullbkgpdf("fullbkgpdf","fullbkgpdf",finalcat);
RooDataSet datacomb ("datacomb", "datacomb", RooArgList(*hmass,finalcat)) ;
RooDataSet *datacombcat = new RooDataSet("data_combcat","",RooArgList(*hmass)) ;
// add the 'combcat' to the list...if more than one cat
if( catnames.size() > 1 ) {
catnames.push_back("combcat");
catdesc.push_back("Combined");
}
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
TString catname = catnames.at(icat);
finalcat.defineType(catname);
// check if we're in a sub-cat or the comb-cat
RooDataSet *data = NULL;
RooDataSet *inData = NULL;
if( icat < (catnames.size() - 1) || catnames.size() == 1) { // this is NOT the last cat (which is by construction the combination)
inData = (RooDataSet*)win->data(TString("data_mass_")+catname);
if( !inData ) {
std::cerr<<" ERROR: Could not find dataset > data_mass_"<<catname.Data()<<" < in input workspace."<<std::endl;
return;
}
data = new RooDataSet(TString("data_")+catname,"",*hmass,Import(*inData)); // copy the dataset (why?)
// append the data to the combined data...
RooDataSet *datacat = new RooDataSet(TString("datacat")+catname,"",*hmass,Index(finalcat),Import(catname,*data)) ;
datacomb.append(*datacat);
datacombcat->append(*data);
// normalization for this category
RooRealVar *nbkg = new RooRealVar(TString::Format("CMS_hgg_%s_bkgshape_norm",catname.Data()),"",800.0,0.0,25e3);
// we keep track of the normalizario vars only for N-1 cats, naming convetnions hystoric...
if( catnames.size() > 2 && icat < (catnames.size() - 2) ) {
RooRealVar* cbkg = new RooRealVar(TString::Format("cbkg%s",catname.Data()),"",0.0,0.0,1e3);
cbkg->removeRange();
normu_vec.push_back(cbkg);
normList.add(*cbkg);
}
/// generate the Bernstrin polynomial (FIX-ME: add possibility ro create other models...)
fstBernModel* theBGmodel = new fstBernModel(hmass, polorder[icat], icat, catname); // using my dedicated class...
std::cout<<" model name is "<<theBGmodel->getPdf()->GetName()<<std::endl;
RooAbsPdf* bkgshape = theBGmodel->getPdf(); // the BG shape
RooAbsPdf* bkgpdf = new RooExtendPdf(TString("bkgpdf")+catname,"",*bkgshape,*nbkg); // the extended PDF
// add the extedned PDF to the RooSimultaneous holding all models...
fullbkgpdf.addPdf(*bkgpdf,catname);
// store the NON-EXTENDED PDF for usgae to compute the error bands later..
pdfShape_vec.push_back(bkgshape);
pdf_vec .push_back(bkgpdf);
data_vec .push_back(data);
} else {
data = datacombcat; // we're looking at the last cat (by construction the combination)
data_vec.push_back(data);
// sum up all the cts PDFs for combined PDF
RooArgList subpdfs;
for (int ipdf=0; ipdf<pdf_vec.size(); ++ipdf) {
subpdfs.add(*pdf_vec.at(ipdf));
}
RooAddPdf* bkgpdf = new RooAddPdf(TString("bkgpdf")+catname,"",subpdfs);
pdfShape_vec.push_back(bkgpdf);
pdf_vec .push_back(bkgpdf); // I don't think this is really needed though....
}
// generate the binned dataset (to be put into the workspace... just in case...)
RooDataHist *databinned = new RooDataHist(TString("databinned_")+catname,"",*hmass,*data);
wOut->import(*data);
wOut->import(*databinned);
}
std::cout<<" ***************** "<<std::endl;
// fit the RooSimultaneous to the combined dataset -> (we could also fit each cat separately)
fullbkgpdf.fitTo(datacomb,Strategy(1),Minos(kFALSE),Save(kTRUE));
RooFitResult *fullbkgfitres = fullbkgpdf.fitTo(datacomb,Strategy(2),Minos(kFALSE),Save(kTRUE));
// in principle we're done now, so store the results in the output workspace
wOut->import(datacomb);
wOut->import(fullbkgpdf);
wOut->import(*fullbkgfitres);
std::cout<<" ***************** "<<std::endl;
if( verbose ) wOut->Print();
std::cout<<" ***************** "<<std::endl;
wOut->writeToFile("bkgdatawithfit.root") ;
if( verbose ) {
printf("IntLumi = %5f\n",intLumi->getVal());
printf("ndata:\n");
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
printf("%i ",data_vec.at(icat)->numEntries());
}
printf("\n");
}
// --------------------------------------------------------------------------------------------
// Now comesd the plotting
// chage the Statistics style...
gStyle->SetOptStat(1110);
// we want to plot in 1GeV bins (apparently...)
UInt_t nbins = (UInt_t) (massmax-massmin);
// here we'll store the curves for the bands...
std::vector<RooCurve*> fitcurves;
// loop again over the cats
TCanvas **canbkg = new TCanvas*[catnames.size()];
RooPlot** plot = new RooPlot*[catnames.size()];
TLatex** lat = new TLatex*[catnames.size()];
TLatex** lat2 = new TLatex*[catnames.size()];
std::cout<<" beofre plotting..."<<std::endl;
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
TString catname = catnames.at(icat);
std::cout<<" trying to plot #"<<icat<<std::endl;
// plot the data and the fit
canbkg[icat] = new TCanvas;
plot [icat] = hmass->frame(Bins(nbins),Range("fitrange"));
std::cout<<" trying to plot #"<<icat<<std::endl;
// first plot the data invisibly... and put the fitted BG model on top...
data_vec .at(icat)->plotOn(plot[icat],RooFit::LineColor(kWhite),MarkerColor(kWhite),Invisible());
pdfShape_vec.at(icat)->plotOn(plot[icat],RooFit::LineColor(kRed),Range("fitrange"),NormRange("fitrange"));
std::cout<<" trying to plot #"<<icat<<std::endl;
// if toggled on, plot also the Data visibly
if( !blinded ) {
data_vec.at(icat)->plotOn(plot[icat]);
}
std::cout<<" trying to plot #"<<icat<<std::endl;
// some cosmetics...
plot[icat]->SetTitle("");
plot[icat]->SetMinimum(0.0);
plot[icat]->SetMaximum(1.40*plot[icat]->GetMaximum());
plot[icat]->GetXaxis()->SetTitle("m_{#gamma#gamma} (GeV/c^{2})");
plot[icat]->Draw();
std::cout<<" trying to plot #"<<icat<<std::endl;
// legend....
TLegend *legmc = new TLegend(0.68,0.70,0.97,0.90);
legmc->AddEntry(plot[icat]->getObject(2),"Data","LPE");
legmc->AddEntry(plot[icat]->getObject(1),"Bkg Model","L");
// this part computes the 1/2-sigma bands.
TGraphAsymmErrors *onesigma = NULL;
TGraphAsymmErrors *twosigma = NULL;
std::cout<<" trying *** to plot #"<<icat<<std::endl;
RooAddition* sumcatsnm1 = NULL;
if ( dobands ) { //&& icat == (catnames.size() - 1) ) {
onesigma = new TGraphAsymmErrors();
twosigma = new TGraphAsymmErrors();
// get the PDF for this cat from the vector
RooAbsPdf *thisPdf = pdfShape_vec.at(icat);
// get the nominal fir curve
RooCurve *nomcurve = dynamic_cast<RooCurve*>(plot[icat]->getObject(1));
fitcurves.push_back(nomcurve);
bool iscombcat = ( icat == (catnames.size() - 1) && catnames.size() > 1);
RooAbsData *datanorm = ( iscombcat ? &datacomb : data_vec.at(icat) );
// this si the nornmalization in the 'sliding-window' (i.e. per 'test-bin')
RooRealVar *nlim = new RooRealVar(TString::Format("nlim%s",catnames.at(icat).Data()),"",0.0,0.0,10.0);
nlim->removeRange();
if( iscombcat ) {
// ----------- HISTORIC NAMING ----------------------------------------
sumcatsnm1 = new RooAddition("sumcatsnm1","",normList); // summing all normalizations epect the last Cat
// this is the normlization of the last Cat
RooFormulaVar *nlast = new RooFormulaVar("nlast","","TMath::Max(0.1,@0-@1)",RooArgList(*nlim,*sumcatsnm1));
// ... and adding it ot the list of norms
normu_vec.push_back(nlast);
}
//if (icat == 1 && catnames.size() == 2) continue; // only 1 cat, so don't need combination
for (int i=1; i<(plot[icat]->GetXaxis()->GetNbins()+1); ++i) {
// this defines the 'binning' we use for the error bands
double lowedge = plot[icat]->GetXaxis()->GetBinLowEdge(i);
double upedge = plot[icat]->GetXaxis()->GetBinUpEdge(i);
double center = plot[icat]->GetXaxis()->GetBinCenter(i);
// get the nominal value at the center of the bin
double nombkg = nomcurve->interpolate(center);
nlim->setVal(nombkg);
hmass->setRange("errRange",lowedge,upedge);
// this is the new extended PDF whith the normalization restricted to the bin-area
RooAbsPdf *extLimPdf = NULL;
if( iscombcat ) {
extLimPdf = new RooSimultaneous("epdf","",finalcat);
// loop over the cats and generate temporary extended PDFs
for (int jcat=0; jcat<(catnames.size()-1); ++jcat) {
RooRealVar *rvar = dynamic_cast<RooRealVar*>(normu_vec.at(jcat));
if (rvar) rvar->setVal(fitcurves.at(jcat)->interpolate(center));
RooExtendPdf *ecpdf = new RooExtendPdf(TString::Format("ecpdf%s",catnames.at(jcat).Data()),"",*pdfShape_vec.at(jcat),*normu_vec.at(jcat),"errRange");
static_cast<RooSimultaneous*>(extLimPdf)->addPdf(*ecpdf,catnames.at(jcat));
}
} else
extLimPdf = new RooExtendPdf("extLimPdf","",*thisPdf,*nlim,"errRange");
RooAbsReal *nll = extLimPdf->createNLL(*datanorm,Extended(),NumCPU(1));
RooMinimizer minim(*nll);
minim.setStrategy(0);
double clone = 1.0 - 2.0*RooStats::SignificanceToPValue(1.0);
double cltwo = 1.0 - 2.0*RooStats::SignificanceToPValue(2.0);
if (iscombcat) minim.setStrategy(2);
minim.migrad();
if (!iscombcat) {
minim.minos(*nlim);
}
else {
minim.hesse();
nlim->removeAsymError();
}
if( verbose )
printf("errlo = %5f, errhi = %5f\n",nlim->getErrorLo(),nlim->getErrorHi());
onesigma->SetPoint(i-1,center,nombkg);
onesigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
// to get the 2-sigma bands...
minim.setErrorLevel(0.5*pow(ROOT::Math::normal_quantile(1-0.5*(1-cltwo),1.0), 2)); // the 0.5 is because qmu is -2*NLL
// eventually if cl = 0.95 this is the usual 1.92!
if (!iscombcat) {
minim.migrad();
minim.minos(*nlim);
}
else {
nlim->setError(2.0*nlim->getError());
nlim->removeAsymError();
}
twosigma->SetPoint(i-1,center,nombkg);
twosigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
// for memory clean-up
delete nll;
delete extLimPdf;
}
hmass->setRange("errRange",massmin,massmax);
if( verbose )
onesigma->Print("V");
// plot[icat] the error bands
twosigma->SetLineColor(kGreen);
twosigma->SetFillColor(kGreen);
twosigma->SetMarkerColor(kGreen);
twosigma->Draw("L3 SAME");
onesigma->SetLineColor(kYellow);
onesigma->SetFillColor(kYellow);
onesigma->SetMarkerColor(kYellow);
onesigma->Draw("L3 SAME");
plot[icat]->Draw("SAME");
// and add the error bands to the legend
legmc->AddEntry(onesigma,"#pm1 #sigma","F");
legmc->AddEntry(twosigma,"#pm2 #sigma","F");
}
std::cout<<" trying ***2 to plot #"<<icat<<std::endl;
// rest of the legend ....
legmc->SetBorderSize(0);
legmc->SetFillStyle(0);
legmc->Draw();
lat[icat] = new TLatex(103.0,0.9*plot[icat]->GetMaximum(),TString::Format("#scale[0.7]{#splitline{CMS preliminary}{#sqrt{s} = %.1f TeV L = %.2f fb^{-1}}}",theCMenergy,intLumi->getVal()));
lat2[icat] = new TLatex(103.0,0.75*plot[icat]->GetMaximum(),catdesc.at(icat));
lat[icat] ->Draw();
lat2[icat]->Draw();
// -------------------------------------------------------
// save canvas in different formats
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".pdf"));
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".eps"));
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".root"));
}
return;
}
void fitpeaks(int bin){
switch (bin)
{
case 0:
cut_="abs(upsRapidity)<2.4";
//cut_="( (muPlusPt>3.5 && abs(muPlusEta)<1.6) || (muPlusPt>2.5 && abs(muPlusEta)>=1.6 && abs(muPlusEta)<2.4) ) && ( (muMinusPt>3.5 && abs(muMinusEta)<1.6) || (muMinusPt>2.5 && abs(muMinusEta)>=1.6 && abs(muMinusEta)<2.4) ) && abs(upsRapidity)<2.0"; //pp acceptance for Upsilon
suffix_="";
f2Svs1S_pp->setVal(0.5569);
//f2Svs1S_pp->setVal(0);
f3Svs1S_pp->setVal(0.4140);
//f3Svs1S_pp->setVal(0);
break;
case 1:
cut_="abs(upsRapidity)>=0.0 && abs(upsRapidity)<1.2";
suffix_="_eta0-12"; binw_=0.14;
break;
case 2:
cut_="abs(upsRapidity)>=1.2 && abs(upsRapidity)<2.4";
suffix_="_eta12-24"; binw_=0.14;
break;
case 3:
cut_="Centrality>=0 && Centrality<2";
suffix_="_cntr0-5"; binw_=0.14;
break;
case 4:
cut_="Centrality>=2 && Centrality<4";
suffix_="_cntr5-10"; binw_=0.14;
break;
case 5:
cut_="Centrality>=4 && Centrality<8";
suffix_="_cntr10-20"; binw_=0.14;
break;
case 6:
cut_="Centrality>=8 && Centrality<12";
suffix_="_cntr20-30"; binw_=0.14;
break;
case 7:
cut_="Centrality>=12 && Centrality<16";
suffix_="_cntr30-40"; binw_=0.14;
break;
case 8:
cut_="Centrality>=16 && Centrality<20";
suffix_="_cntr40-50"; binw_=0.14;
break;
case 9:
cut_="Centrality>=20 && Centrality<50";
suffix_="_cntr50-100"; binw_=0.14;
break;
case 10:
cut_="Centrality>=20 && Centrality<24";
suffix_="_cntr50-60"; binw_=0.14;
break;
case 11:
cut_="Centrality>=0 && Centrality<8";
suffix_="_cntr0-20"; binw_=0.1;
break;
case 12:
cut_="Centrality>=16 && Centrality<50";
suffix_="_cntr40-100"; binw_=0.14;
break;
case 13:
cut_="Centrality>=8 && Centrality<50";
suffix_="_cntr20-100"; binw_=0.1;
break;
default:
cout<<"error in binning"<<endl;
break;
}
cout << "oniafitter processing"
<< "\n\tInput: \t" << finput
<< "\n\tresults:\t" << figs_
<< endl;
ofstream outfile("fitresults.out", ios_base::app);
outfile<<endl<<"**********"<<suffix_<<"**********"<<endl<<endl;
//read the data
TFile f(finput,"read");
gDirectory->Cd(finput+":/"+dirname_);
TTree* theTree = (TTree*)gROOT->FindObject("UpsilonTree");
TTree* allsignTree = (TTree*)gROOT->FindObject("UpsilonTree_allsign");
if (PR_plot) {TRKROT = 1; PbPb=1;}
if (TRKROT) TTree* trkRotTree = (TTree*)gROOT->FindObject("UpsilonTree_trkRot");
RooRealVar* mass = new RooRealVar("invariantMass","#mu#mu mass",mmin_,mmax_,"GeV/c^{2}");
RooRealVar* upsPt = new RooRealVar("upsPt","p_{T}(#Upsilon)",0,60,"GeV");
RooRealVar* upsEta = new RooRealVar("upsEta", "upsEta" ,-7,7);
RooRealVar* upsRapidity = new RooRealVar("upsRapidity", "upsRapidity" ,-2.4,2.4);
RooRealVar* vProb = new RooRealVar("vProb", "vProb" ,0.05,1.00);
RooRealVar* QQsign = new RooRealVar("QQsign", "QQsign" ,-1,5);
RooRealVar* weight = new RooRealVar("weight", "weight" ,-2,2);
if (PbPb) RooRealVar* Centrality = new RooRealVar("Centrality", "Centrality" ,0,40);
RooRealVar* muPlusPt = new RooRealVar("muPlusPt","muPlusPt",muonpTcut,50);
RooRealVar* muPlusEta = new RooRealVar("muPlusEta","muPlusEta",-2.5,2.5);
RooRealVar* muMinusPt = new RooRealVar("muMinusPt","muMinusPt",muonpTcut,50);
RooRealVar* muMinusEta = new RooRealVar("muMinusEta","muMinusEta",-2.5,2.5);
//import unlike-sign data set
RooDataSet* data0, *data, *likesignData0, *likesignData, *TrkRotData0, *TrkRotData;
if (PbPb) data0 = new RooDataSet("data","data",theTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*Centrality,*muPlusPt,*muMinusPt));
//data0 = new RooDataSet("data","data",theTree,RooArgSet(*mass,*upsRapidity,*upsPt,*muPlusPt,*muMinusPt,*QQsign,*weight));
else data0 = new RooDataSet("data","data",theTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*muPlusPt,*muMinusPt,*muPlusEta,*muMinusEta));
data0->Print();
data = ( RooDataSet*) data0->reduce(Cut(cut_));
data->Print();
//import like-sign data set
if (PbPb) likesignData0 = new RooDataSet("likesignData","likesignData",allsignTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*Centrality,*muPlusPt,*muMinusPt,*QQsign));
else likesignData0 = new RooDataSet("likesignData","likesignData",allsignTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*muPlusPt,*muMinusPt,*QQsign));
likesignData0->Print();
likesignData = ( RooDataSet*) likesignData0->reduce(Cut(cut_+" && QQsign != 0"));
likesignData->Print();
//import track-rotation data set
if (TRKROT) {
if (PbPb) TrkRotData0 = new RooDataSet("TrkRotData","TrkRotData",trkRotTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*Centrality,*muPlusPt,*muMinusPt,*QQsign));
else TrkRotData0 = new RooDataSet("TrkRotData","TrkRotData",trkRotTree,RooArgSet(*mass,*upsRapidity,*upsPt,*vProb,*muPlusPt,*muMinusPt,*QQsign));
TrkRotData0->Print();
if (PR_plot && RAA) TrkRotData = ( RooDataSet*) TrkRotData0->reduce(Cut(cut_+" && upsPt < 8.1"));
else if (PR_plot && !RAA) TrkRotData = ( RooDataSet*) TrkRotData0->reduce(Cut(cut_+" && upsPt < 7.07"));
else TrkRotData = ( RooDataSet*) TrkRotData0->reduce(Cut(cut_+" && QQsign != 0"));
TrkRotData->Print();
}
mass->setRange("R1",7.0,10.2);
mass->setRange("R2",7,14);
mass->setRange("R3",10.8,14);
const double M1S = 9.46; //upsilon 1S pgd mass value
const double M2S = 10.02; //upsilon 2S pgd mass value
const double M3S = 10.35; //upsilon 3S pgd mass value
RooRealVar *mean = new RooRealVar("#mu_{#Upsilon(1S)}","#Upsilon mean",M1S,M1S-0.1,M1S+0.1);
RooRealVar *shift21 = new RooRealVar("shift2","mass diff #Upsilon(1,2S)",M2S-M1S);
RooRealVar *shift31 = new RooRealVar("shift3","mass diff #Upsilon(1,3S)",M3S-M1S);
RooRealVar *mscale = new RooRealVar("mscale","mass scale factor",1.,0.7,1.3);
mscale->setConstant(kTRUE); /* the def. parameter value is fixed in the fit */
RooFormulaVar *mean1S = new RooFormulaVar("mean1S","@0",
RooArgList(*mean));
RooFormulaVar *mean2S = new RooFormulaVar("mean2S","@0+@1*@2",
RooArgList(*mean,*mscale,*shift21));
RooFormulaVar *mean3S = new RooFormulaVar("mean3S","@0+@1*@2",
RooArgList(*mean,*mscale,*shift31));
RooRealVar *sigma1 = new RooRealVar("sigma","Sigma_1",0.10,0.01,0.30); //detector resolution
RooRealVar *sigma2 = new RooRealVar("#sigma_{#Upsilon(1S)}","Sigma_1S",0.08,0.01,0.30); //Y(1S) resolution
RooFormulaVar *reso1S = new RooFormulaVar("reso1S","@0" ,RooArgList(*sigma2));
RooFormulaVar *reso2S = new RooFormulaVar("reso2S","@0*10.023/9.460",RooArgList(*sigma2));
RooFormulaVar *reso3S = new RooFormulaVar("reso3S","@0*10.355/9.460",RooArgList(*sigma2));
/// to describe final state radiation tail on the left of the peaks
RooRealVar *alpha = new RooRealVar("alpha","tail shift",0.982,0,2.4); // minbias fit value
//RooRealVar *alpha = new RooRealVar("alpha","tail shift",1.6,0.2,4); // MC value
RooRealVar *npow = new RooRealVar("npow","power order",2.3,1,3); // MC value
npow ->setConstant(kTRUE);
if (!fitMB) alpha->setConstant(kTRUE);
// relative fraction of the two peak components
RooRealVar *sigmaFraction = new RooRealVar("sigmaFraction","Sigma Fraction",0.3,0.,1.);
sigmaFraction->setVal(0);
sigmaFraction->setConstant(kTRUE);
/// Upsilon 1S
//RooCBShape *gauss1S1 = new RooCBShape ("gauss1S1", "FSR cb 1s",
// *mass,*mean1S,*sigma1,*alpha,*npow);
RooCBShape *gauss1S2 = new RooCBShape ("gauss1S2", "FSR cb 1s",
*mass,*mean1S,*reso1S,*alpha,*npow);
//RooAddPdf *sig1S = new RooAddPdf ("sig1S","1S mass pdf",
// RooArgList(*gauss1S1,*gauss1S2),*sigmaFraction);
//mean->setVal(9.46);
//mean->setConstant(kTRUE);
sigma1->setVal(0);
sigma1->setConstant(kTRUE);
if (!fitMB) {
sigma2->setVal(width_); //fix the resolution
sigma2->setConstant(kTRUE);
}
/// Upsilon 2S
RooCBShape *gauss2S1 = new RooCBShape ("gauss2S1", "FSR cb 2s",
*mass,*mean2S,*sigma1,*alpha,*npow);
RooCBShape *gauss2S2 = new RooCBShape ("gauss2S2", "FSR cb 2s",
*mass,*mean2S,*reso2S,*alpha,*npow);
RooAddPdf *sig2S = new RooAddPdf ("sig2S","2S mass pdf",
RooArgList(*gauss2S1,*gauss2S2),*sigmaFraction);
/// Upsilon 3S
RooCBShape *gauss3S1 = new RooCBShape ("gauss3S1", "FSR cb 3s",
*mass,*mean3S,*sigma1,*alpha,*npow);
RooCBShape *gauss3S2 = new RooCBShape ("gauss3S2", "FSR cb 3s",
*mass,*mean3S,*reso3S,*alpha,*npow);
RooAddPdf *sig3S = new RooAddPdf ("sig3S","3S mass pdf",
RooArgList(*gauss3S1,*gauss3S2),*sigmaFraction);
/// Background
RooRealVar *bkg_a1 = new RooRealVar("bkg_{a1}", "background a1", 0, -2, 2);
RooRealVar *bkg_a2 = new RooRealVar("bkg_{a2}", "background a2", 0, -1, 1);
//RooRealVar *bkg_a3 = new RooRealVar("bkg_{a3}", "background a3", 0, -1, 1);
RooAbsPdf *bkgPdf = new RooChebychev("bkg","background",
*mass, RooArgList(*bkg_a1,*bkg_a2));
//bkg_a1->setVal(0);
//bkg_a1->setConstant(kTRUE);
//bkg_a2->setVal(0);
//bkg_a2->setConstant(kTRUE); //set constant for liner background
// only sideband region pdf, using RooPolynomial instead of RooChebychev for multiple ranges fit
RooRealVar *SB_bkg_a1 = new RooRealVar("SB bkg_{a1}", "background a1", 0, -1, 1);
RooRealVar *SB_bkg_a2 = new RooRealVar("SB bkg_{a2}", "background a2", 0, -1, 1);
RooAbsPdf *SB_bkgPdf = new RooPolynomial("SB_bkg","side-band background",
*mass, RooArgList(*SB_bkg_a1,*SB_bkg_a2));
//SB_bkg_a1->setVal(0);
//SB_bkg_a1->setConstant(kTRUE);
//SB_bkg_a2->setVal(0);
//SB_bkg_a2->setConstant(kTRUE);
/// Combined pdf
int nt = 100000;
//bool fitfraction = true;
RooRealVar *nbkgd = new RooRealVar("N_{bkg}","nbkgd",nt*0.75,0,10*nt);
RooRealVar *SB_nbkgd = new RooRealVar("SB N_{bkg}","SB_nbkgd",nt*0.75,0,10*nt);
RooRealVar *nsig1f = new RooRealVar("N_{#Upsilon(1S)}","nsig1S",nt*0.25,0,10*nt);
/*
//use the YIELDs of 2S and 3S as free parameters
RooRealVar *nsig2f = new RooRealVar("N_{#Upsilon(2S)}","nsig2S", nt*0.25,-1*nt,10*nt);
RooRealVar *nsig3f = new RooRealVar("N_{#Upsilon(3S)}","nsig3S", nt*0.25,-1*nt,10*nt);
*/
//use the RATIOs of 2S and 3S as free parameters
RooRealVar *f2Svs1S = new RooRealVar("N_{2S}/N_{1S}","f2Svs1S",0.21,-0.1,1);
//RooRealVar *f3Svs1S = new RooRealVar("N_{3S}/N_{1S}","f3Svs1S",0.0,-0.1,0.5);
RooRealVar *f23vs1S = new RooRealVar("N_{2S+3S}/N_{1S}","f23vs1S",0.45,-0.1,1);
RooFormulaVar *nsig2f = new RooFormulaVar("nsig2S","@0*@1", RooArgList(*nsig1f,*f2Svs1S));
//RooFormulaVar *nsig3f = new RooFormulaVar("nsig3S","@0*@1", RooArgList(*nsig1f,*f3Svs1S));
RooFormulaVar *nsig3f = new RooFormulaVar("nsig3S","@0*@2-@0*@1", RooArgList(*nsig1f,*f2Svs1S,*f23vs1S));
//f3Svs1S->setConstant(kTRUE);
//force the ratio to the pp value
f2Svs1S_pp->setConstant(kTRUE);
f3Svs1S_pp->setConstant(kTRUE);
RooFormulaVar *nsig2f_ = new RooFormulaVar("nsig2S_pp","@0*@1", RooArgList(*nsig1f,*f2Svs1S_pp));
RooFormulaVar *nsig3f_ = new RooFormulaVar("nsig3S_pp","@0*@1", RooArgList(*nsig1f,*f3Svs1S_pp));
//only sideband region pdf, using RooPolynomial instead of RooChebychev for multiple ranges fit
RooAbsPdf *SB_pdf = new RooAddPdf ("SB_pdf","sideband background pdf",
RooArgList(*SB_bkgPdf),
RooArgList(*SB_nbkgd));
//only signal region pdf, using RooPolynomial instead of RooChebychev for multiple ranges fit
RooAbsPdf *S_pdf = new RooAddPdf ("S_pdf","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*SB_bkgPdf),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*SB_nbkgd));
//parameters for likesign
RooRealVar m0shift("turnOn","turnOn",8.6,0,20.) ;
RooRealVar width("width","width",2.36,0,20.) ;
RooRealVar par3("decay","decay",6.8, 0, 20.) ;
RooGaussian* m0shift_constr;
RooGaussian* width_constr;
RooGaussian* par3_constr;
RooRealVar *nLikesignbkgd = new RooRealVar("NLikesign_{bkg}","nlikesignbkgd",nt*0.75,0,10*nt);
if (TRKROT) {
nLikesignbkgd->setVal(TrkRotData->sumEntries());
nLikesignbkgd->setError(sqrt(TrkRotData->sumEntries()));
}
else {
nLikesignbkgd->setVal(likesignData->sumEntries());
nLikesignbkgd->setError(sqrt(likesignData->sumEntries()));
}
if (LS_constrain) {
RooGaussian* nLikesignbkgd_constr = new RooGaussian("nLikesignbkgd_constr","nLikesignbkgd_constr",*nLikesignbkgd,RooConst(nLikesignbkgd->getVal()),RooConst(nLikesignbkgd->getError()));
}
else nLikesignbkgd->setConstant(kTRUE);
RooFormulaVar *nResidualbkgd = new RooFormulaVar("NResidual_{bkg}","@0-@1",RooArgList(*nbkgd,*nLikesignbkgd));
switch (bkgdModel) {
case 1 : //use error function to fit the like-sign, then fix the shape and normailization,
RooGenericPdf *LikeSignPdf = new RooGenericPdf("Like-sign","likesign","exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",RooArgList(*mass,m0shift,width,par3));
if (TRKROT) RooFitResult* fit_1st = LikeSignPdf->fitTo(*TrkRotData,Save()) ;
else RooFitResult* fit_1st = LikeSignPdf->fitTo(*likesignData,Save()) ; // likesign data
//LikeSignPdf.fitTo(*data) ; // unlikesign data
//fit_1st->Print();
if (LS_constrain) {
m0shift_constr = new RooGaussian("m0shift_constr","m0shift_constr",m0shift,RooConst(m0shift.getVal()),RooConst(m0shift.getError()));
width_constr = new RooGaussian("width_constr","width_constr",width,RooConst(width.getVal()),RooConst(width.getError()));
par3_constr = new RooGaussian("par3_constr","par3_constr",par3,RooConst(par3.getVal()),RooConst(par3.getError()));
//m0shift_constr = new RooGaussian("m0shift_constr","m0shift_constr",m0shift,RooConst(7.9),RooConst(0.34*2));
//width_constr = new RooGaussian("width_constr","width_constr",width,RooConst(2.77),RooConst(0.38*2));
//par3_constr = new RooGaussian("par3_constr","par3_constr",par3,RooConst(6.3),RooConst(1.0*2));
}
else {
m0shift.setConstant(kTRUE);
width.setConstant(kTRUE);
par3.setConstant(kTRUE);
}
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf,*LikeSignPdf),
RooArgList(*nResidualbkgd,*nLikesignbkgd));
//RooArgList(*LikeSignPdf),
//RooArgList(*nbkgd));
break;
case 2 : //use RooKeysPdf to smooth the like-sign, then fix the shape and normailization
if (TRKROT) RooKeysPdf *LikeSignPdf = new RooKeysPdf("Like-sign","likesign",*mass,*TrkRotData,3,1.5);
else RooKeysPdf *LikeSignPdf = new RooKeysPdf("Like-sign","likesign",*mass,*likesignData,3,1.7);
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf,*LikeSignPdf),
RooArgList(*nResidualbkgd,*nLikesignbkgd));
break;
case 3 : //use error function to fit the unlike-sign directly
RooGenericPdf *LikeSignPdf = new RooGenericPdf("Like-sign","likesign","exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",RooArgList(*mass,m0shift,width,par3));
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*LikeSignPdf),
RooArgList(*nbkgd));
break;
case 4 : //use polynomial to fit the unlike-sign directly
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf),
RooArgList(*nbkgd));
break;
case 5 : //use ( error function + polynomial ) to fit the unlike-sign directly
RooGenericPdf *LikeSignPdf = new RooGenericPdf("Like-sign","likesign","exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",RooArgList(*mass,m0shift,width,par3));
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf,*LikeSignPdf),
RooArgList(*nResidualbkgd,*nLikesignbkgd));
break;
default :
break;
}
//pdf with fixed ratio of the pp ratio
RooAbsPdf *pdf_pp = new RooAddPdf ("pdf_pp","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*pdf_combinedbkgd),
RooArgList(*nsig1f,*nsig2f_,*nsig3f_,*nbkgd));
//the nominal fit with default pdf
if (LS_constrain) {
RooAbsPdf *pdf_unconstr = new RooAddPdf ("pdf_unconstr","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*pdf_combinedbkgd),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*nbkgd));
RooProdPdf *pdf = new RooProdPdf ("pdf","total constr pdf",
RooArgSet(*pdf_unconstr,*m0shift_constr,*width_constr,*par3_constr,*nLikesignbkgd_constr));
RooFitResult* fit_2nd = pdf->fitTo(*data,Constrained(),Save(kTRUE),Extended(kTRUE),Minos(doMinos));
}
else {
RooAbsPdf *pdf = new RooAddPdf ("pdf","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*pdf_combinedbkgd),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*nbkgd));
RooFitResult* fit_2nd = pdf->fitTo(*data,Save(kTRUE),Extended(kTRUE),Minos(doMinos));
}
//plot
TCanvas c; c.cd();
int nbins = ceil((mmax_-mmin_)/binw_);
RooPlot* frame = mass->frame(Bins(nbins),Range(mmin_,mmax_));
data->plotOn(frame,Name("theData"),MarkerSize(0.8));
pdf->plotOn(frame,Name("thePdf"));
if (plotLikeSign) {
if (TRKROT) TrkRotData->plotOn(frame,Name("theLikeSignData"),MarkerSize(0.8),MarkerColor(kMagenta),MarkerStyle(22));
else likesignData->plotOn(frame,Name("theLikeSignData"),MarkerSize(0.8),MarkerColor(kRed),MarkerStyle(24));
//LikeSignPdf->plotOn(frame,Name("theLikeSign"),VisualizeError(*fit_1st,1),FillColor(kOrange));
//LikeSignPdf->plotOn(frame,Name("theLikeSign"),LineColor(kRed));
}
RooArgSet * pars = pdf->getParameters(data);
//RooArgSet * pars = LikeSignPdf->getParameters(likesignData);
//calculate chi2 in a mass range
float bin_Min = (8.2-mmin_)/binw_;
float bin_Max = (10.8-mmin_)/binw_;
int binMin = ceil(bin_Min);
int binMax = ceil(bin_Max);
int nfloatpars = pars->selectByAttrib("Constant",kFALSE)->getSize();
float myndof = ceil((10.8-8.2)/binw_) - nfloatpars;
cout<<binMin<<" "<<binMax<<" "<<nfloatpars<<" "<<myndof<<endl;
double mychsq = frame->mychiSquare("thePdf","theData",nfloatpars,true,binMin,binMax)*myndof;
//double mychsq = frame->mychiSquare("theLikeSign","theLikeSignData",nfloatpars,true,binMin,binMax)*myndof;
/*
int nfloatpars = pars->selectByAttrib("Constant",kFALSE)->getSize();
float myndof = frame->GetNbinsX() - nfloatpars;
double mychsq = frame->chiSquare("theLikeSign","theLikeSignData",nfloatpars)*myndof;
*/
//plot parameters
if(plotpars) {
paramOn_ = "_paramOn";
pdf->paramOn(frame,Layout(0.15,0.6,0.4),Layout(0.5,0.935,0.97),Label(Form("#chi^{2}/ndf = %2.1f/%2.0f", mychsq,myndof)));
}
/*
mass->setRange("R1S",8.8,9.7);
mass->setRange("R2S",9.8,10.2);
//pdf_combinedbkgd->fitTo(*data,Range("R1,R3"),Constrained(),Save(kTRUE),Extended(kTRUE),Minos(doMinos));
RooAbsReal* integral_1S = pdf_combinedbkgd->createIntegral(*mass,NormSet(*mass),Range("R1S")) ;
cout << "1S bkgd integral = " << integral_1S->getVal() * (nbkgd->getVal()) << endl ;
RooAbsReal* integral_2S = pdf_combinedbkgd->createIntegral(*mass,NormSet(*mass),Range("R2S")) ;
cout << "2S bkgd integral = " << integral_2S->getVal() * (nbkgd->getVal()) << endl ;
cout << "1S range count: " << data->sumEntries("invariantMass","R1S") <<endl;
cout << "2S range count: " << data->sumEntries("invariantMass","R2S") <<endl;
cout << "1S signal yield: " << data->sumEntries("invariantMass","R1S") - integral_1S->getVal() * (nbkgd->getVal()) << endl;
cout << "2S signal yield: " << data->sumEntries("invariantMass","R2S") - integral_2S->getVal() * (nbkgd->getVal()) << endl;
*/
outfile<<"Y(1S) yield : = "<<nsig1f->getVal()<<" +/- "<<nsig1f->getError()<<endl<<endl;
outfile<<"free parameter = "<< nfloatpars << ", mychi2 = " << mychsq << ", ndof = " << myndof << endl << endl;
//draw the fit lines and save plots
data->plotOn(frame,Name("theData"),MarkerSize(0.8));
pdf->plotOn(frame,Components("bkg"),Name("theBkg"),LineStyle(5),LineColor(kGreen));
pdf->plotOn(frame,Components("pdf_combinedbkgd"),LineStyle(kDashed));
if (plotLikeSign) {
if (TRKROT) pdf->plotOn(frame,Components("Like-sign"),Name("theLikeSign"),LineStyle(9),LineColor(kMagenta));
else pdf->plotOn(frame,Components("Like-sign"),Name("theLikeSign"),LineStyle(9),LineColor(kRed));
}
pdf->plotOn(frame,Name("thePdf"));
data->plotOn(frame,MarkerSize(0.8));
if (plotLikeSign) {
if (TRKROT) TrkRotData->plotOn(frame,Name("theTrkRotData"),MarkerSize(0.8),MarkerColor(kMagenta),MarkerStyle(22));
else likesignData->plotOn(frame,Name("theLikeSignData"),MarkerSize(0.8),MarkerColor(kRed),MarkerStyle(24));
}
frame->SetTitle( "" );
frame->GetXaxis()->SetTitle("m_{#mu^{+}#mu^{-}} (GeV/c^{2})");
frame->GetXaxis()->CenterTitle(kTRUE);
frame->GetYaxis()->SetTitleOffset(1.3);
if (PR_plot && RAA) frame->GetYaxis()->SetRangeUser(0,1200);
//frame->GetYaxis()->SetLabelSize(0.05);
frame->Draw();
//plot parameters
if(!plotpars) {
paramOn_ = "";
TLatex latex1;
latex1.SetNDC();
if (PbPb) {
latex1.DrawLatex(0.46,1.-0.05*3,"CMS PbPb #sqrt{s_{NN}} = 2.76 TeV");
latex1.DrawLatex(0.5,1.-0.05*4.9,"L_{int} = 150 #mub^{-1}");
switch (bin) {
case 0: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 0-100%, |y| < 2.4"); break;
case 3: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 0-5%, |y| < 2.4"); break;
case 4: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 5-10%, |y| < 2.4"); break;
case 5: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 10-20%, |y| < 2.4"); break;
case 6: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 20-30%, |y| < 2.4"); break;
case 7: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 30-40%, |y| < 2.4"); break;
case 8: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 40-50%, |y| < 2.4"); break;
case 9: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 50-100%, |y| < 2.4"); break;
default; break;
}
}
else {
