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Drell-Yan analysis Procedure

This twiki documents the most important steps of the Drell-Yan cross section measurement. It is intended to familiarize you with the technical aspects of the analysis procedure. 

Step 1: Producing ntuples

  • Samples
  • The CMSSW_53X MC samples are used for 8 TeV analysis. Below is the list of starting GEN-SIM-RECO samples used in the muon and electro analyses:

DYToMuMuM-10To20 & Powheg-Pythia6 & CT10TuneZ2star 

DYToMuMuM-20 & Powheg-Pythia6 & CT10TuneZ2star 

DYToMuMuM-200 & Powheg-Pythia6 & TuneZ2star 

DYToMuMuM-400 & Powheg-Pythia6 & TuneZ2star 

DYToMuMuM-500 & Powheg-Pythia6 & TuneZ2star 

DYToMuMuM-700 & Powheg-Pythia6 & TuneZ2star 

DYToMuMuM-800 & Powheg-Pythia6 & TuneZ2star 

DYToMuMuM-1000 & Powheg-Pythia6 & TuneZ2star

DYToMuMuM-1500 & Powheg-Pythia6 & TuneZ2star 

DYToMuMuM-2000 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-10To20 & Powheg-Pythia6 & CT10TuneZ2star 

DYToEEM-20 & Powheg-Pythia6 & CT10TuneZ2star 

DYToEEM-200 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-400 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-500 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-700 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-800 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-1000 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-1500 & Powheg-Pythia6 & TuneZ2star 

DYToEEM-2000 & Powheg-Pythia6 & TuneZ2star 

DYToTauTauM-10To20 & Powheg-Pythia6-tauola & TuneZ2star 

DYToTauTauM-20 & Powheg-Pythia6-tauola &CT10TuneZ2star 

WJetsToLNu & madgraph-tarball & TuneZ2star 

WWJetsTo2L2Nu &  madgraph-tauola & TuneZ2star 

WZJetsTo2L2Q &  madgraph-tauola& TuneZ2star 

WZJetsTo3LNu &  madgraph-tauola& TuneZ2star 

ZZJetsTo2L2Nu &  madgraph-tauola& TuneZ2star 

ZZJetsTo2L2Q &  madgraph-tauola& TuneZ2star 

ZZJetsTo4L &  madgraph-tauola& TuneZ2star 

TTMtt-700to1000 &  Powheg-tauola& TuneZ2star 

TTMtt-1000toInf  &  Powheg-tauola& TuneZ2star 

TTJetsFullLeptMGDecays &  madgraph& TuneZ2star 

TTJetsFullLeptMGDecays &  madgraph& TuneZ2star 

TT & Powheg-tauola & TuneZ2star 

TW & Powheg-tauola & TuneZ2star 

TbarW & Powheg-tauola & TuneZ2star 

QCDPt-15to20MuPt5Enriched & Pythia6 &TuneZ2star 

QCDPt-20to30MuPt5Enriched & Pythia6 &TuneZ2star 

QCDPt-30to50MuPt5Enriched & Pythia6 &TuneZ2star 

QCDPt-50to80MuPt5Enriched & Pythia6 &TuneZ2star

QCDPt-80to120MuPt5Enriched & Pythia6 & TuneZ2star

QCDPt-120to150MuPt5Enriched & Pythia6 &TuneZ2star 

QCDPt-150MuPt5Enriched & Pythia6 & TuneZ2star 

MC generation is 53X

  • DATA:
  • We use SingleMu and DoubleMu Primary Datasets (PD), January2013 ReReco version

    /DoubleMu/Run2012A-22Jan2013-v1/AOD : 190645-193621


    /DoubleMuParked/Run2012B-22Jan2013-v1/AOD : 193834-196531


    /DoubleMuParked/Run2012C-22Jan2013-v1/AOD : 198049-203742


    /DoubleMuParked/Run2012D-22Jan2013-v1/AOD : 203777-208686


    /SingleMu/Run2012A-22Jan2013-v1/AOD : 190645-193621

    /SingleMu/Run2012B-22Jan2013-v1/AOD : 193834-196531

    /SingleMu/Run2012C-22Jan2013-v1/AOD : 198049-203742

    /SingleMu/Run2012D-22Jan2013-v1/AOD : 203777-208686









  • JSONs: Cert190456-2086868TeV22Jan2013ReRecoCollisions12JSON.txt, Jan22Jan2013
  • Relevant software: CMSSW_5_3_3_patch2
    • Use latest global tags for MC data for a given release as documented here
    • DY analysis package (Purdue), DY analysis package (MIT)
cmsrel CMSSW_5_3_3_patch2
cd CMSSW_5_3_3_patch2/src
git cms-addpkg DataFormats/PatCandidates
git cms-addpkg PhysicsTools/PatAlgos
git cms-addpkg PhysicsTools/PatUtils
git clone DimuonAnalysis/DYPackage
scram b -j8
export DYWorkDir=$CMSSW_RELEASE_BASE/src/DimuonAnalysis/DYPackage
cd $DYWorkDir/ntuples

To simply perform a local test of the ntuple-maker run:


to produce the ntuples over full dataset use CRAB:

crab -create -submit -cfg crab.cfg
crab -get all -c <crab_0_datetime>

Step 3: Event Selection

Once the ntuples are ready, one can proceed to the actual physics analysis. The first step of every analysis is the event selection. Currently, we use the so-called cut-based approach to discriminate between signal and background. For more on event selection please read chapter 3 in the analysis note CMS-AN-11-013. Before starting to run a macro, set up the working area. Find all the necessary scripts in:

cd $DYWorkDir/test/ControlPlots

and precisely follow the recipe below preserving folder structure recommended in the recipe:

1. copy the Ntuples in rootfiles directory, splitting by trigger path used at the level of skimming (as we might use a combination!): e.g. rootfiles/HLT_Mu15/

2. modify the file

cd rootfiles

change this to the full path to your rootfiles directory:
$dir = "/work/asvyatko/Work/DYanalysis/dataAnalysis10/analysis/rootfiles";
choose the trigger scenario you wish to use:
$trig = "HLT_Mu15"; // trigger name (directory name)

Run the shell script:

then all chain_* files will be created in the roorfiles directory.
  • After you are done with creating chains (I assume you are in ./rootfiles directory) do:
cd ..
cvs co -d ControlPlots UserCode/Purdue/DYAnalysis/AnalysisMacros/ControlPlots
cd ControlPlots
#note: for the sake of convenience add the alias for this directory in your ~/.profile, like:

Before running the macros, we need to fix few things which are changing frequently for our analysis:

  • Mass range and binning:
    • for the early stage of 2011 analysis we keep the 2010 binning [15,20,30,40,50,60,76,86,96,106,120,150,200,600]
  • Trigger selection:
    • See the presentation on event selection for 2011
    • Thus, for 2011 we consider a combination of Double muon trigger and a combination of single isolated muon triggers can be used as a cross-check. Use three following combinations:
      • HLT_Mu15, HLT_Mu24, HLT_Mu30
      • HLT_IsoMu15, HLT_IsoMu17, HLT_IsoMu24
      • DoubleMu6, DoubleMu7, Mu13_Mu8
  • Offline selection: Baseline event selection has not changed compared to 2010 analysis, see
    • we will consider moving to PF muons and PF isolation: this study is in progress right now

To produce the invariant mass plot do use the analyse2.C macro, which calls the TSelector for the DY analysis (called EventSelector):

root -l analyse2.C

The macro allows to run on multiple cores.

By performing minor changes inside the EventSelector one can calculate the efficiency weighted invariant mass distribution (which is used to estimate the corection factor as a function of invariant mass). Inside the EventSelector.C set

#define CORRECT_FOR_EFF true <- change to true if you want to use it for efficiency correction estimation
#define CORRECTION_TYPE "trig" <- choose the efficiency type recoid, iso or trig

To produce dimuon kinematic distributions run


To produce other control plot (for all the event selection variables used in the analysis, as documented in the note), use:

 root -l ControlPlots.C++

There are few macros that help us to optimize the cuts. These macros calculate the statistical significance and the uncertainty on the cross-section. Statistical significance is defined as :

S = N_sig/sqrt(N_sig+N_bkg) and normally determined from MC. As you can infer, it scales with luminosity as ~sqrt(Lumi). There are other definitions of significance used in the analyses sometimes (see for instance CMS-TDR). To run, check out just two additional macros (I assume you didn't leave ./ControlPlots directory)


The first macro will create a txt file with an per mass bin values of signal and background. The second macro will histogram the output. These macros are adjusted to optimize the acceptance cuts, but with minimal changes it can optimize any other cut we use, and it is possible to change style to conform with the rest of plots in the note.

Note: root doesn not create output directories by itself so you should create a corresponding directory for output txt files like:


Q1: Check data/MC agreement for each plot, look for discrepancies.

Checkpoint1 With the macros described above you should be able to *reproduce* following plots from the CMS-AN-11-013: 1,3-14, 17-29,51.

Note: for the 23,25-29 macros have different style and were produce with PU sample.

Note: plots 20-22 are reproducible by optimization macros but have different style.

Step 4: Acceptance and Efficiency estimation

Another constituent of the cross-section measurement is the acceptance-efficiency.

  • Acceptance is determined using GEN level information

How to run:

cd $DYWorkDir/AccEffMCtruth

The script will produce the root file with histograms corresponding to the mass and rapidity spectra after the acceptance cuts, selection cuts or both which are then used to calculate the acceptances, efficiencies and acceptance-efficiency products with and without pileup and FEWZ reweighing by executing:

root -l plotMCtruth.C
root -l plotMCtruth_2D.C

The macro output a root file starting with out1* or out2* containing the histograms corresponding to the acceptance, efficiency and their product.

Next, the data-driven efficiency corrections are applied. The details on the factorization and the application of correction factors are documented here , and can be found in this talk. With the current factorization scheme we measure four following efficiencies:

  • Trigger, Reconstruction+ID, isolation:
    • We use the officiela TagAndProbe package
  • How to run (on top of CMSSW 425 or later):

    addpkg CommonTools/ParticleFlow V00-02-07                        
    addpkg CommonTools/RecoAlgos V00-03-13                           
    addpkg DataFormats/PatCandidates V06-04-18                      
    addpkg MuonAnalysis/MuonAssociators V01-13-00                     
    addpkg MuonAnalysis/TagAndProbe HEAD                         
    addpkg PhysicsTools/Configuration V00-10-16                       
    addpkg PhysicsTools/PatAlgos V08-06-38                            
    addpkg PhysicsTools/PatExamples V00-05-22                         
    addpkg PhysicsTools/SelectorUtils V00-03-17                       
    addpkg PhysicsTools/TagAndProbe HEAD                         
    addpkg RecoMuon/MuonIdentification V01-19-00
    cvs co -d TagAndProbe UserCode/ASvyatkovskiy/TagAndProbe
  • The procedure goes in two steps: 
    • T&P tree production -> rerun seldom (ideally once), it depends only on the definitions of the tag and probe
cd TagAndProbe
  • If you haven't produce TP trees you can always use the ready ones located there:


  • fitting: separate job for trigger and all the muonID related efficiencies -> reran frequently and usually interactively (change binning, definitions)
  • All the latest macros/configs can be found here: UserCode/ASvyatkovskiy/TagAndProbe
  • Isolation: RandomCone - currently, code is private and not possible to use.

After familiarizing yourself with the TagAndProbe package, you need to produce the muon efficiencies as a function of pT and eta. You do not need this in the analysis, but rather to understand if everything you are doing is correct. After you are done with that, produce the 2D efficiency pT-eta map (it is alredy produced in one go when running To do that use the simple root macros (adjust i/o, not user friendly yet!):

root -l idDataMC_4xy.C
root -l triggerMuonDataMC_4xy.C

And to produce 2D efficiency maps and correction factors do:

 root -l perBinTable.C

The final step here is to produce the efficiency as function of invariant mass and the efficiency correction factor as a function of invariant mass.

cvs co UserCode/Purdue/DYAnalysis/AnalysisMacros/Correction
root -l efficiencyMass_newTmp.C
root -l correctionMass_newTmp.C

Note: you need to produce all the correction 2D maps on 2 previous steps, if you haven't succeeded you can use what we used for publication, txt files are located here:


Checkpoint3 With the macros describe in the step5 section it is possible to reproduce the following plots from the CMS-AN-11-013 note: 15-16, 39-42 and tables 11-12

Note: plot 40 was produced with LKTC method, code for which is currently not public and not possible to be retrieved from the authors. Currently (2011 data) the result is consistent with that obtained with Tag-And-Probe.

Step 6: Background estimation

QCD data driven background estimation

There are various methods employed to estimate the QCD background in a data-driven way (QCD is currently the only background estimated not from MC). The most important are the template fit method and the weight map method: carefully read chapter6 of the CMS-AN-11-013 for more details on the methods.

Reweighting method. First of all, read the quick description (in addition to what is written in the note) and also see this presentation .

cd ..
cvs co -d ControlPlots UserCode/Purdue/DYAnalysis/AnalysisMacros/QCDEstimation

There are few steps in this method. First of all, create a pT-eta weight look-up table indicating probability of a muon to be isolated as a function of muon pT-eta:

cd ControlPlots
root -l WeightMapFiller.C

The next step is to view the map, and to test it on the sample of dimuons and single muons:

root -l testWeightMapDouble.C
root -l testWeightMapSingle.C

Other methods used for the QCD background estimation in the note are the SS/OS pair method and template fit method (carefully read the note on the description!). For the SS-OS method, which uses the discriminative power of the isolation variable, considering classes of events having 2, 1or 0 isolated muon. You can get the plot by running:

cvs co UserCode/ASvyatkovskiy/QCD_Background
cd UserCode/ASvyatkovskiy/QCD_Background
root -l bgSS_OS.C

As for the template fit method:

root -l
.L auto_fits.C; fitAll()
#It needs input parameters - the important ones are
#bool etaBelow1_2 = false;
#bool fitFirstMu = true;
#bool singleMu = false;

Note: the original input files can be found at:


Checkpoint: this macros will allow one to reproduce the plots 45-48 from the note as well as tables 13-15 from the note

ABCD method

We estimate QCD background using ABCD method in order to improve our systematic uncertainty on the background estimation. ABCD method is very simple.

1) choose 2 variables: assume two variables are independent

2) assume the fraction should be same if there is no correlation: N_A / N_B = N_C / N_D

3) In our study, use two variables: sign of muon pair, muon isolation

4) QCD fraction in each region has a dependence. We produce the correction factor for each region: B, C, D

5) Produce N_B, N_C, N_D from data sample, and estimate N_A from them at the end (applying the correction factors)

In UserCode/Purdue/DYAnalysis/AnalysisMacros/ABCDmethod

QCDFrac.C: to produce correction factors for each region

ABCD2vari.C: to produce the ABCD results. The correction factors from the QCDFrac.C are plugged in this macro as an input.

ttbar data driven background estimation

We employ the so-called e-mu data driven background estimation method. See the following comprehensive talk for more details on the method. Currently the procedure to apply this method consists of 2 steps:

1) produce the root files with histograms

2) run the macros on the root files produced

For both steps one needs to check out the following tags:

V00-05-00      MuonAnalysis/Examples                            
V01-13-00      MuonAnalysis/MuonAssociators                     
V01-01-11      RecoVertex/VertexTools                           
V00-05-00      SHarper/HEEPAnalyzer                             
V00-11-00      SUSYBSMAnalysis/Zprime2muAnalysis                
V00-03-00      UserCode/Examples 

The highleted tags are important for step2).

Following is the description of how to produce the root files.

The mother script file is Zprime2muAnalysis/test/DataMCSpectraComparison/

Instructions related to this script file are at

The short instruction is this:

python submit testing no_data

or when you are ready

 python submit

Wait for root files to be done. Currently it is configured to have histograms with selection marked 'VBTF' as what we have in DY2011.

Below I describe the step2 in detail. Check out addtional macros, and copy them to your working directory:

cvs co UserCode/Purdue/DYAnalysis/AnalysisMacros/TTbarEstimation
cp UserCode/Purdue/DYAnalysis/AnalysisMacros/TTbarEstimation/*
cd SUSYBSMAnalysis/Zprime2muAnalysis/test/DataMCSpectraComparison

Make sure the paths to datafiles inside the macros are pointing to the location of the root files you have produced. To produce the control plots for emu and mumu mass spectra use


To produce the correction factors run:


And finally, the MC expectation vs. data driven method prediction plots are produced with:


A good agreement between data and MC for both the mumu and emu spectra is necessary for a method to work reliably.

Step 7: Unfolding

Unfolding is applied to correct for migration of entries between bins caused by mass resolution effects (FSR correction is taken into account as a separate step).  For use in the Drell-Yan analysis, the choice for unfolding is matrix inversion. Provides a common interface between channels for symmetry and ease in combination and systematic studies.

To do any unfolding with MC, this requires 3 things:

  • Producing the response matrix
  • Making the histogram of measured events
  • Making the true histogram (clearly not used/available when unfolding data)

First, one can do some exercise, for that use script that demonstrates how the unfolding/fore-folding object works.

cvs co UserCode/kypreos/drellYan2010/unfolding
cd UserCode/kypreos/drellYan2010/unfolding/
root -l test/testExpo.C++

To get back the pulls:

root -l test/testPulls.C++

The macros in the note are produced with the following:

 cvs co /UserCode/Purdue/DYAnalysis/Unfolding

1. To rpoduce the response matrix:

root -l unfoldingObs.C

2. To produce the unfolded yield plot do

root -l yield.C

Checkpoint7 with this macros one should be able to reproduce the plot 49-50 from the note and Tables 17-18 (note, the table 18 uses the background yield result from the background section)

Step 8: FSR correction

The effect of FSR is manifested by photon emission off the final state muon. It leads to change of the dimuon invariant mass and as a result a dimuon has invariant mass distinct from the propagator (or Z/gamma*) mass.

For our analysis we estimate the effect of FSR and the corresponding correction by estimating the bin-by-bin correction in invariant mass bins. Which is done by comparing the pre-FSR and the post-FSR spectra. The pre-FSR spectrum can be obtained by requiring mother of muon to be Z/gamma*, post FSR spectrum is when the mother is whatever.. The corresponding plots in the note are: 52-55 they all can be calculated with the information avaialble in the ntuple using

root -l InvMassFSR.C++

To get the FSR histograms one needs to turno on calculateFSR flag on.

Checkpoint: this macro will allow one to get plots 52-55 from the note

Step 9: Systematic uncertainty estimation

There are various sources of systematics affecting our analysis: the PDF, theoretical modeling uncertainty, efficiency estimation uncertainty, background estimation, unfolding etc.

For the background estimation, with the data driven method we estimate the systematic uncertainty as the difference between the result obtained with the method and that

expected from MC per mass bin. Corresponding numbers are obtained with the

macro (see the recipe in the step 6 section).

PDF uncertainty estimation. The recipe for the method currently used (step by step).
Reweight the PDF using the current existing MC samples as implemented in CMSSW. First, check out the necessary packages:

scramv1 p CMSSW CMSSW_4_2_3
cvsco -r CMSSW_4_2_3 ElectroWeakAnalysis/Utilities

then replace the LHAPDF library as described here to the current up-to-date one:
or you can directly change in:
with above path:

touch $CMSSW_BASE/src/ElectroWeakAnalysis/Utilities/BuildFile.xml
scramv1 b
cd ElectroWeakAnalysis/Utilities/test

then change the input file in and run:


With the up-to-date LHAPDF, one can use CT10, MSTW2008*, CTEQ66, NNPDF2.0, and other PDF sets.

Efficiency estimation uncertainty. The current method for efficiency estimation in the DY analysis is following: we estimate the MC truth efficiency and then we apply the efficiency correction map (Pt-eta) extracted using the data-driven tag and probe method applied to data and MC to weight the MC events. The systematic uncertainty associated with the Tag-and-Probe efficiency estimation is due to line-shape modelling, the difference between fit and counting and due to the binning. The two first are calculated inside the macros described in Step5. The binning systematic uncertainty is estimated using the following macro:


it takes as input the root files having the histogram with efficiency correction as a function of invariant mass with two binnings (to estimate the binning uncertainty), the other sources of uncertainty are also accessed.

Step 10: Plotting the results

The main result of the measurement is the cross-section ratio or r (and R) shape. We distinguish R and r shapes (see the note chapter9 for details on the definition and also see Figures 64). The figure 64 shows the shape R for theory and measurement (for two independent trigger scenarios). It relies on the theoretical cross-section measurement (1-2GeV bin), the final numbers for acceptance correction and also the final numbers for cross-section measurement. To give a clearer feeling of what this plot depends on I name the tables that are used to produce the number in the plot 64:

Table 21-24: Theoretical predictions

Tables 25-26: Measurement

Table 5-10: Acceptance-efficiency corrections

To run the code one simply needs:

cvs co UserCode/Purdue/DYAnalysis/AnalysisMacros/GautierMacro
cp UserCode/Purdue/DYAnalysis/AnalysisMacros/GautirMacro/* $CONTROL_PLOTS_DIR
root -l theory_plot.C++

Use Gautier style macros to get the same plots with different style:

root -l DY.C
root -l plot.C

To get all the up to date values for the shape r/R use:

cvs co UserCode/Purdue/DYAnalysis/AnalysisMacros/ShapeR./shapeDY.make

Among the requirements to style of the results presented is to put the measurement point to the weighted position (i.e. the location of the point inside the bin makes the integral over sub-bins equal from both sides). The following macro can be used to calculate these positions do in root:


A lot of intersting information can be retrieved from the Zprime JTERM SHORT and LONG exercises (which are constructed along the same lines as this tutorial).

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