///////////////////////////////////////////////////////////////////////// // // 'ADDITION AND CONVOLUTION' RooFit tutorial macro #201 // // Composite p.d.f with signal and background component // // pdf = f_bkg * bkg(x,a0,a1) + (1-fbkg) * (f_sig1 * sig1(x,m,s1 + (1-f_sig1) * sig2(x,m,s2))) // // // 07/2008 - Wouter Verkerke // ///////////////////////////////////////////////////////////////////////// #ifndef __CINT__ #include "RooGlobalFunc.h" #endif #include "RooRealVar.h" #include "RooDataSet.h" #include "RooGaussian.h" #include "RooChebychev.h" #include "RooAddPdf.h" #include "TCanvas.h" #include "TAxis.h" #include "RooPlot.h" using namespace RooFit ; void rf201_composite() { // S e t u p c o m p o n e n t p d f s // --------------------------------------- // 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)) ; //////////////////////////////////////////////////// // M E T H O D 1 - T w o R o o A d d P d f s // //////////////////////////////////////////////////// // A d d s i g n a l c o m p o n e n t s // ------------------------------------------ // 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) ; // A d d s i g n a l a n d b a c k g r o u n d // ------------------------------------------------ // 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) ; // S a m p l e , f i t a n d p l o t m o d e l // --------------------------------------------------- // Generate a data sample of 1000 events in x from model RooDataSet *data = model.generate(x,1000) ; // Fit model to data model.fitTo(*data) ; // Plot data and PDF overlaid RooPlot* xframe = x.frame(Title("Example of composite pdf=(sig1+sig2)+bkg")) ; data->plotOn(xframe) ; model.plotOn(xframe) ; // Overlay the background component of model with a dashed line model.plotOn(xframe,Components(bkg),LineStyle(kDashed)) ; // Overlay the background+sig2 components of model with a dotted line model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted)) ; // Print structure of composite p.d.f. model.Print("t") ; //////////////////////////////////////////////////////////////////////////////////////////////////// // M E T H O D 2 - O n e R o o A d d P d f w i t h r e c u r s i v e f r a c t i o n s // //////////////////////////////////////////////////////////////////////////////////////////////////// // Construct sum of models on one go using recursive fraction interpretations // // model2 = bkg + (sig1 + sig2) // RooAddPdf model2("model","g1+g2+a",RooArgList(bkg,sig1,sig2),RooArgList(bkgfrac,sig1frac),kTRUE) ; // NB: Each coefficient is interpreted as the fraction of the // left-hand component of the i-th recursive sum, i.e. // // sum4 = A + ( B + ( C + D) with fraction fA, fB and fC expands to // // sum4 = fA*A + (1-fA)*(fB*B + (1-fB)*(fC*C + (1-fC)*D)) // P l o t r e c u r s i v e a d d i t i o n m o d e l // --------------------------------------------------------- model2.plotOn(xframe,LineColor(kRed),LineStyle(kDashed)) ; model2.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineColor(kRed),LineStyle(kDashed)) ; model2.Print("t") ; // Draw the frame on the canvas new TCanvas("rf201_composite","rf201_composite",600,600) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ; }