rf306_condpereventerrors.C File Reference

Detailed Description

Multidimensional models: conditional p.d.f.

with per-event errors

 
 
␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m 
                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
                All rights reserved, please read http://roofit.sourceforge.net/license.txt
 
[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
 **********
 **    1 **SET PRINT           1
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 **********
 **    2 **SET NOGRAD
 **********
 PARAMETER DEFINITIONS:
    NO.   NAME         VALUE      STEP SIZE      LIMITS
     1 bias         0.00000e+00  2.00000e+00   -1.00000e+01  1.00000e+01
     2 sigma        1.00000e+00  4.50000e-01    1.00000e-01  1.00000e+01
 **********
 **    3 **SET ERR         0.5
 **********
 **********
 **    4 **SET PRINT           1
 **********
 **********
 **    5 **SET STR           1
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 NOW USING STRATEGY  1: TRY TO BALANCE SPEED AGAINST RELIABILITY
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 **    6 **MIGRAD        1000           1
 **********
 FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
 START MIGRAD MINIMIZATION.  STRATEGY  1.  CONVERGENCE WHEN EDM .LT. 1.00e-03
 FCN=23876.4 FROM MIGRAD    STATUS=INITIATE        8 CALLS           9 TOTAL
                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       
  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  bias         0.00000e+00   2.00000e+00   2.01358e-01  -1.70342e+02
   2  sigma        1.00000e+00   4.50000e-01   1.63378e-01   8.62474e+01
                               ERR DEF= 0.5
 MIGRAD MINIMIZATION HAS CONVERGED.
 MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=23876.2 FROM MIGRAD    STATUS=CONVERGED      30 CALLS          31 TOTAL
                     EDM=3.03467e-08    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                   STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  bias         5.26463e-03   1.72103e-02   1.83632e-04   1.00797e-01
   2  sigma        9.87130e-01   2.04183e-02   7.70435e-04   2.52620e-03
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5
  2.962e-04 -4.402e-06 
 -4.402e-06  4.169e-04 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2
        1  0.01253   1.000 -0.013
        2  0.01253  -0.013  1.000
 **********
 **    7 **SET ERR         0.5
 **********
 **********
 **    8 **SET PRINT           1
 **********
 **********
 **    9 **HESSE        1000
 **********
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=23876.2 FROM HESSE     STATUS=OK             12 CALLS          43 TOTAL
                     EDM=3.12688e-08    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                INTERNAL      INTERNAL  
  NO.   NAME      VALUE            ERROR       STEP SIZE       VALUE   
   1  bias         5.26463e-03   1.72105e-02   3.67263e-05   5.26463e-04
   2  sigma        9.87130e-01   2.04260e-02   3.08174e-05  -9.62778e-01
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5
  2.962e-04 -4.786e-06 
 -4.786e-06  4.172e-04 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2
        1  0.01361   1.000 -0.014
        2  0.01361  -0.014  1.000
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gm1_conv_exp(-abs(@0)/@1)_dt_tau_[decay_gm]_Int[dt,dterr]) using numeric integrator RooIntegrator1D to calculate Int(dterr)
[#1] INFO:Plotting -- RooAbsReal::plotOn(decay_gm) plot on dt averages using data variables (dterr)
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(decay_gmDataWgtAvg) constructing data weighted average of function decay_gm_Norm[dt] over 100 data points of (dterr) with a total weight of 10000
.........................................................................................................................................................................................................................

 
#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();
}

Author

07/2008 - Wouter Verkerke

Definition in file rf306_condpereventerrors.C.