rf702_efficiencyfit_2D.C File Reference

Detailed Description

Special pdf's: unbinned maximum likelihood fit of an efficiency eff(x) function to a dataset D(x,cut), cut is a category encoding a selection whose efficiency as function of x should be described by eff(x)

 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooCategory.h"
#include "RooEfficiency.h"
#include "RooPolynomial.h"
#include "RooProdPdf.h"
#include "RooFormulaVar.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH1.h"
#include "RooPlot.h"
using namespace RooFit;
 
void rf702_efficiencyfit_2D(bool flat = false)
{
   // C o n s t r u c t   e f f i c i e n c y   f u n c t i o n   e ( x , y )
   // -----------------------------------------------------------------------
 
   // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
   RooRealVar x("x", "x", -10, 10);
   RooRealVar y("y", "y", -10, 10);
 
   // Efficiency function eff(x;a,b)
   RooRealVar ax("ax", "ay", 0.6, 0, 1);
   RooRealVar bx("bx", "by", 5);
   RooRealVar cx("cx", "cy", -1, -10, 10);
 
   RooRealVar ay("ay", "ay", 0.2, 0, 1);
   RooRealVar by("by", "by", 5);
   RooRealVar cy("cy", "cy", -1, -10, 10);
 
   RooFormulaVar effFunc("effFunc", "((1-ax)+ax*cos((x-cx)/bx))*((1-ay)+ay*cos((y-cy)/by))",
                         RooArgList(ax, bx, cx, x, ay, by, cy, y));
 
   // Acceptance state cut (1 or 0)
   RooCategory cut("cut", "cutr", { {"accept", 1}, {"reject", 0} });
 
   // C o n s t r u c t   c o n d i t i o n a l    e f f i c i e n c y   p d f   E ( c u t | x , y )
   // ---------------------------------------------------------------------------------------------
 
   // Construct efficiency pdf eff(cut|x)
   RooEfficiency effPdf("effPdf", "effPdf", effFunc, cut, "accept");
 
   // G e n e r a t e   d a t a   ( x , y , c u t )   f r o m   a   t o y   m o d e l
   // -------------------------------------------------------------------------------
 
   // Construct global shape pdf shape(x) and product model(x,cut) = eff(cut|x)*shape(x)
   // (These are _only_ needed to generate some toy MC here to be used later)
   RooPolynomial shapePdfX("shapePdfX", "shapePdfX", x, RooConst(flat ? 0 : -0.095));
   RooPolynomial shapePdfY("shapePdfY", "shapePdfY", y, RooConst(flat ? 0 : +0.095));
   RooProdPdf shapePdf("shapePdf", "shapePdf", RooArgSet(shapePdfX, shapePdfY));
   RooProdPdf model("model", "model", shapePdf, Conditional(effPdf, cut));
 
   // Generate some toy data from model
   RooDataSet *data = model.generate(RooArgSet(x, y, cut), 10000);
 
   // F i t   c o n d i t i o n a l   e f f i c i e n c y   p d f   t o   d a t a
   // --------------------------------------------------------------------------
 
   // Fit conditional efficiency pdf to data
   effPdf.fitTo(*data, ConditionalObservables(RooArgSet(x, y)), PrintLevel(-1));
 
   // P l o t   f i t t e d ,   d a t a   e f f i c i e n c y
   // --------------------------------------------------------
 
   // Make 2D histograms of all data, selected data and efficiency function
   TH1 *hh_data_all = data->createHistogram("hh_data_all", x, Binning(8), YVar(y, Binning(8)));
   TH1 *hh_data_sel = data->createHistogram("hh_data_sel", x, Binning(8), YVar(y, Binning(8)), Cut("cut==cut::accept"));
   TH1 *hh_eff = effFunc.createHistogram("hh_eff", x, Binning(50), YVar(y, Binning(50)));
 
   // Some adjustment for good visualization
   hh_data_all->SetMinimum(0);
   hh_data_sel->SetMinimum(0);
   hh_eff->SetMinimum(0);
   hh_eff->SetLineColor(kBlue);
 
   // Draw all frames on a canvas
   TCanvas *ca = new TCanvas("rf702_efficiency_2D", "rf702_efficiency_2D", 1200, 400);
   ca->Divide(3);
   ca->cd(1);
   gPad->SetLeftMargin(0.15);
   hh_data_all->GetZaxis()->SetTitleOffset(1.8);
   hh_data_all->Draw("lego");
   ca->cd(2);
   gPad->SetLeftMargin(0.15);
   hh_data_sel->GetZaxis()->SetTitleOffset(1.8);
   hh_data_sel->Draw("lego");
   ca->cd(3);
   gPad->SetLeftMargin(0.15);
   hh_eff->GetZaxis()->SetTitleOffset(1.8);
   hh_eff->Draw("surf");
 
   return;
}

 
[#0] WARNING:Generation -- RooAcceptReject::ctor(effPdf_Int[]_Norm[cut]) WARNING: performing accept/reject sampling on a p.d.f in 2 dimensions without prior knowledge on maximum value of p.d.f. Determining maximum value by taking 200000 trial samples. If p.d.f contains sharp peaks smaller than average distance between trial sampling points these may be missed and p.d.f. may be sampled incorrectly.
[#0] WARNING:Generation -- RooAcceptReject::ctor(effPdf_Int[]_Norm[cut]): WARNING: 200000 trial samples requested by p.d.f for 2-dimensional accept/reject sampling, this may take some time
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization

Date

February 2018

Authors

Clemens Lange, Wouter Verkerke (C++ version)

Definition in file rf702_efficiencyfit_2D.C.