void limit() { //*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* //*-* //*-* This program demonstrates the computation of 95 % C.L. limits. //*-* It uses a set of randomly created histograms. //*-* //*-* Written by Christophe.Delaere@cern.ch on 21.08.02 //*-* //*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* // Create a new canvas. TCanvas *c1 = new TCanvas("c1","Dynamic Filling Example",200,10,700,500); c1->SetFillColor(42); // Create some histograms TH1D* background = new TH1D("background","The expected background",30,-4,4); TH1D* signal = new TH1D("signal","the expected signal",30,-4,4); TH1D* data = new TH1D("data","some fake data points",30,-4,4); background->SetFillColor(48); signal->SetFillColor(41); data->SetMarkerStyle(21); data->SetMarkerColor(kBlue); background->Sumw2(); // needed for stat uncertainty signal->Sumw2(); // needed for stat uncertainty // Fill histograms randomly TRandom r; Float_t bg,sig,dt; for (Int_t i = 0; i < 25000; i++) { bg = r.Gaus(0,1); sig = r.Gaus(1,.2); background->Fill(bg,0.02); signal->Fill(sig,0.001); } for (Int_t i = 0; i < 500; i++) { dt = r.Gaus(0,1); data->Fill(dt); } THStack *hs = new THStack("hs","Signal and background compared to data..."); hs->Add(background); hs->Add(signal); hs->Draw("hist"); data->Draw("PE1,Same"); c1->Modified(); c1->Update(); c1->GetFrame()->SetFillColor(21); c1->GetFrame()->SetBorderSize(6); c1->GetFrame()->SetBorderMode(-1); c1->Modified(); c1->Update(); gSystem->ProcessEvents(); // Compute the limits cout << "Computing limits... " << endl; TLimitDataSource* mydatasource = new TLimitDataSource(signal,background,data); TConfidenceLevel *myconfidence = TLimit::ComputeLimit(mydatasource,50000); cout << "CLs : " << myconfidence->CLs() << endl; cout << "CLsb : " << myconfidence->CLsb() << endl; cout << "CLb : " << myconfidence->CLb() << endl; cout << "< CLs > : " << myconfidence->GetExpectedCLs_b() << endl; cout << "< CLsb > : " << myconfidence->GetExpectedCLsb_b() << endl; cout << "< CLb > : " << myconfidence->GetExpectedCLb_b() << endl; // Add stat uncertainty cout << endl << "Computing limits with stat systematics... " << endl; TConfidenceLevel *mystatconfidence = TLimit::ComputeLimit(mydatasource,50000,true); cout << "CLs : " << mystatconfidence->CLs() << endl; cout << "CLsb : " << mystatconfidence->CLsb() << endl; cout << "CLb : " << mystatconfidence->CLb() << endl; cout << "< CLs > : " << mystatconfidence->GetExpectedCLs_b() << endl; cout << "< CLsb > : " << mystatconfidence->GetExpectedCLsb_b() << endl; cout << "< CLb > : " << mystatconfidence->GetExpectedCLb_b() << endl; // Add some systematics cout << endl << "Computing limits with systematics... " << endl; TVectorD errorb(2); TVectorD errors(2); TObjArray* names = new TObjArray(); TObjString name1("bg uncertainty"); TObjString name2("sig uncertainty"); names->AddLast(&name1); names->AddLast(&name2); errorb[0]=0.05; // error source 1: 5% errorb[1]=0; // error source 2: 0% errors[0]=0; // error source 1: 0% errors[1]=0.01; // error source 2: 1% TLimitDataSource* mynewdatasource = new TLimitDataSource(); mynewdatasource->AddChannel(signal,background,data,&errors,&errorb,names); TConfidenceLevel *mynewconfidence = TLimit::ComputeLimit(mynewdatasource,50000,true); cout << "CLs : " << mynewconfidence->CLs() << endl; cout << "CLsb : " << mynewconfidence->CLsb() << endl; cout << "CLb : " << mynewconfidence->CLb() << endl; cout << "< CLs > : " << mynewconfidence->GetExpectedCLs_b() << endl; cout << "< CLsb > : " << mynewconfidence->GetExpectedCLsb_b() << endl; cout << "< CLb > : " << mynewconfidence->GetExpectedCLb_b() << endl; // show canonical -2lnQ plots in a new canvas // - The histogram of -2lnQ for background hypothesis (full) // - The histogram of -2lnQ for signal and background hypothesis (dashed) TCanvas *c2 = new TCanvas("c2"); myconfidence->Draw(); // clean up (except histograms and canvas) delete myconfidence; delete mydatasource; delete mystatconfidence; delete mynewconfidence; delete mynewdatasource; }