rf803_mcstudy_addons2.C

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/// \file
/// \ingroup tutorial_roofit_main
/// \notebook -js
/// Validation and MC studies: RooMCStudy - Using the randomizer and profile likelihood add-on models
///
/// \macro_image
/// \macro_code
/// \macro_output
///
/// \date July 2008
/// \author Wouter Verkerke
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooMCStudy.h"
#include "RooRandomizeParamMCSModule.h"
#include "RooDLLSignificanceMCSModule.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH1.h"
#include "TDirectory.h"
 
using namespace RooFit;
 
void rf803_mcstudy_addons2()
{
   // C r e a t e   m o d e l
   // -----------------------
 
   // Simulation of signal and background of top quark decaying into
   // 3 jets with background
 
   // Observable
   RooRealVar mjjj("mjjj", "m(3jet) (GeV)", 100, 85., 350.);
 
   // Signal component (Gaussian)
   RooRealVar mtop("mtop", "m(top)", 162);
   RooRealVar wtop("wtop", "m(top) resolution", 15.2);
   RooGaussian sig("sig", "top signal", mjjj, mtop, wtop);
 
   // Background component (Chebychev)
   RooRealVar c0("c0", "Chebychev coefficient 0", -0.846, -1., 1.);
   RooRealVar c1("c1", "Chebychev coefficient 1", 0.112, -1., 1.);
   RooRealVar c2("c2", "Chebychev coefficient 2", 0.076, -1., 1.);
   RooChebychev bkg("bkg", "combinatorial background", mjjj, RooArgList(c0, c1, c2));
 
   // Composite model
   RooRealVar nsig("nsig", "number of signal events", 53, 0, 1e3);
   RooRealVar nbkg("nbkg", "number of background events", 103, 0, 5e3);
   RooAddPdf model("model", "model", RooArgList(sig, bkg), RooArgList(nsig, nbkg));
 
   // C r e a t e   m a n a g e r
   // ---------------------------
 
   // Configure manager to perform binned extended likelihood fits (Binned(),Extended()) on data generated
   // with a Poisson fluctuation on Nobs (Extended())
   RooMCStudy *mcs = new RooMCStudy(model, mjjj, Binned(), Silence(), Extended(true),
                                    FitOptions(Extended(true), PrintEvalErrors(-1)));
 
   // C u s t o m i z e   m a n a g e r
   // ---------------------------------
 
   // Add module that randomizes the summed value of nsig+nbkg
   // sampling from a uniform distribution between 0 and 1000
   //
   // In general one can randomize a single parameter, or a
   // sum of N parameters, using either a uniform or a Gaussian
   // distribution. Multiple randomization can be executed
   // by a single randomizer module
 
   RooRandomizeParamMCSModule randModule;
   randModule.sampleSumUniform(RooArgSet(nsig, nbkg), 50, 500);
   mcs->addModule(randModule);
 
   // Add profile likelihood calculation of significance. Redo each
   // fit while keeping parameter nsig fixed to zero. For each toy,
   // the difference in -log(L) of both fits is stored, as well
   // a simple significance interpretation of the delta(-logL)
   // using Dnll = 0.5 sigma^2
 
   RooDLLSignificanceMCSModule sigModule(nsig, 0);
   mcs->addModule(sigModule);
 
   // R u n   m a n a g e r ,   m a k e   p l o t s
   // ---------------------------------------------
 
   // Run 1000 experiments. This configuration will generate a fair number
   // of (harmless) MINUIT warnings due to the instability of the Chebychev polynomial fit
   // at low statistics.
   mcs->generateAndFit(500);
 
   // Make some plots
   TH1 *dll_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,dll_nullhypo_nsig", AutoBinning(40), AutoBinning(40));
   TH1 *z_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,significance_nullhypo_nsig", AutoBinning(40), AutoBinning(40));
   TH1 *errnsig_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,nsigerr", AutoBinning(40), AutoBinning(40));
   TH1 *errnsig_vs_nsig = mcs->fitParDataSet().createHistogram("nsig,nsigerr", AutoBinning(40), AutoBinning(40));
 
   // Draw plots on canvas
   TCanvas *c = new TCanvas("rf803_mcstudy_addons2", "rf802_mcstudy_addons2", 800, 800);
   c->Divide(2, 2);
   c->cd(1);
   gPad->SetLeftMargin(0.15);
   dll_vs_ngen->GetYaxis()->SetTitleOffset(1.6);
   dll_vs_ngen->Draw("box");
   c->cd(2);
   gPad->SetLeftMargin(0.15);
   z_vs_ngen->GetYaxis()->SetTitleOffset(1.6);
   z_vs_ngen->Draw("box");
   c->cd(3);
   gPad->SetLeftMargin(0.15);
   errnsig_vs_ngen->GetYaxis()->SetTitleOffset(1.6);
   errnsig_vs_ngen->Draw("box");
   c->cd(4);
   gPad->SetLeftMargin(0.15);
   errnsig_vs_nsig->GetYaxis()->SetTitleOffset(1.6);
   errnsig_vs_nsig->Draw("box");
 
   // Make RooMCStudy object available on command line after
   // macro finishes
   gDirectory->Add(mcs);
}