/// \file /// \ingroup tutorial_roofit /// \notebook /// /// Multidimensional models: conditional p.d.f. with per-event errors /// /// \macro_image /// \macro_output /// \macro_code /// /// \date 07/2008 /// \author Wouter Verkerke #include "RooRealVar.h" #include "RooDataSet.h" #include "RooGaussian.h" #include "RooConstVar.h" #include "RooGaussModel.h" #include "RooDecay.h" #include "RooLandau.h" #include "RooPlot.h" #include "TCanvas.h" #include "TAxis.h" #include "TH2D.h" using namespace RooFit; void rf306_condpereventerrors() { // 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 f a k e ' e x t e r n a l ' d a t a w i t h p e r - e v e n t e r r o r // ------------------------------------------------------------------------------------------------------ // Use landau p.d.f to get somewhat realistic distribution with long tail RooLandau pdfDtErr("pdfDtErr", "pdfDtErr", dterr, RooConst(1), RooConst(0.25)); RooDataSet *expDataDterr = pdfDtErr.generate(dterr, 10000); // S a m p l e d a t a f r o m c o n d i t i o n a l d e c a y _ g m ( d t | d t e r r ) // --------------------------------------------------------------------------------------------- // Specify external dataset with dterr values to use decay_dm as conditional p.d.f. RooDataSet *data = decay_gm.generate(dt, ProtoData(*expDataDterr)); // 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 decay_gm.fitTo(*data, ConditionalObservables(dterr)); // 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_decay = decay_gm.createHistogram("hh_decay", dt, Binning(50), YVar(dterr, Binning(50))); hh_decay->SetLineColor(kBlue); // Plot decay_gm(dt|dterr) at various values of dterr RooPlot *frame = dt.frame(Title("Slices of decay(dt|dterr) at various dterr")); for (Int_t ibin = 0; ibin < 100; ibin += 20) { dterr.setBin(ibin); decay_gm.plotOn(frame, Normalization(5.)); } // Make projection of data an dt RooPlot *frame2 = dt.frame(Title("Projection of decay(dt|dterr) on dt")); data->plotOn(frame2); // Make projection of decay(dt|dterr) on dt. // // Instead of integrating out dterr, make a weighted average of curves // at values dterr_i as given in the external dataset. // (The kTRUE argument bins the data before projection to speed up the process) decay_gm.plotOn(frame2, ProjWData(*expDataDterr, kTRUE)); // Draw all frames on canvas TCanvas *c = new TCanvas("rf306_condpereventerrors", "rf306_condperventerrors", 1200, 400); c->Divide(3); c->cd(1); gPad->SetLeftMargin(0.20); hh_decay->GetZaxis()->SetTitleOffset(2.5); hh_decay->Draw("surf"); c->cd(2); gPad->SetLeftMargin(0.15); frame->GetYaxis()->SetTitleOffset(1.6); frame->Draw(); c->cd(3); gPad->SetLeftMargin(0.15); frame2->GetYaxis()->SetTitleOffset(1.6); frame2->Draw(); }