/// \file /// \ingroup tutorial_roofit /// \notebook /// /// Multidimensional models: full p.d.f. with per-event errors /// /// \macro_code /// /// \date 07/2008 /// \author Wouter Verkerke #include "RooRealVar.h" #include "RooDataSet.h" #include "RooGaussian.h" #include "RooGaussModel.h" #include "RooConstVar.h" #include "RooDecay.h" #include "RooLandau.h" #include "RooProdPdf.h" #include "RooHistPdf.h" #include "RooPlot.h" #include "TCanvas.h" #include "TAxis.h" #include "TH1.h" using namespace RooFit; 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(); }