Table of Contents

• Install stable branch
• Quick description
• The different fitting steps
• The settings file
• Update PU weights

Created by gh-md-toc

Install stable branch

cmsrel CMSSW_11_2_0
cd CMSSW_11_2_0/src
cmsenv
git clone git@github.com:cms-egamma/egm_tnp_analysis.git
cd egm_tnp_analysis
make 

Note: if you modify anything in histUtils.pyx then you need to run make cython-build before make in the previous instructions.

Note: This package does not have any CMSSW dependenies. However, we are using this package inside the CMSSW release just to ensure that its getting appropriate version of gcc, ROOT, RooFit, etc.

Quick description

• Package to handle analysis of tnp trees. The main tool is the python fitter tnpEGM_fitter.py

• The interface between the user and the fitter is solely done via the settings file etc/config/settings.py

  • set the flags (i.e. Working points) that can be tested
  • set the different samples and location
    • set the fitting bins
    • set the different cuts to be used
    • set the output directory

• Help message:

  python tnpEGM_fitter.py --help

• The settings have always to be passed to the fitter

  python tnpEGM_fitter.py etc/config/settings.py

• Several settings*.py files are setup for different eras and are located all in etc/config/

The different fitting steps

Everything will be done for a specific flag (so the settings can be the same for different flags). Hence, the flag to be used must be specified each time (named myWP in following).

1. Create the bining: To each bin is associated a cut that can be tuned bin by bin in the settings.py

2. After setting up the settings.py check bins

   python tnpEGM_fitter.py etc/config/settings.py  --flag myWP --checkBins

   if you need additinal cuts for some bins (cleaning cuts), tune cuts in the settings.py, then recheck.

3. Once satisfied with previous step, create the bining

   python tnpEGM_fitter.py etc/config/settings.py  --flag myWP --createBins

   CAUTION: when recreacting bins, the output directory is overwritten! So be sure to not redo that once you are at step2

4. Create the histograms with the different cuts... this is the longest step. Histograms will not be re-done later

   python tnpEGM_fitter.py etc/config/settings.py --flag myWP --createHists

5. Do your first round of fits.

   KEEP IN MIND: please check that all of the fits have converged correctly and don't allow for extreme low mass tail variations as these can't be considered as valid fits (see S8 in presentation https://indico.cern.ch/event/1288547/contributions/5414376/attachments/2654622/4597206/26052023_RMS_EGamma.pdf)

   1. nominal fit

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit

   2. MC fit to constrain alternate signal parameters [note this is the only MC fit that makes sense]

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --mcSig --altSig

      For some fits where we see one more peak tries to appear one can use --addGaus opton with altSig.

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --mcSig --addGaus --altSig

   3. Alternate signal fit (using constraints from previous fits)

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit  --altSig

      If one used --addGaus option in previous step then in this step you have to use the --addGaus option.

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit  --altSig --addGaus

   4. Alternate background fit (using constraints from previous fits)

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit  --altBkg

   5. Alternate signal + background fit (using constraints from previous fits)

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit  --altSigBkg

   6. Check fits and redo failed ones. (there is a web index.php in the plot directory to vizualize from the web)

      • can redo a given bin using its bin number ib. The bin number can be found from --checkBins, directly in the ouput dir (or web interface)

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --iBin ib

      the initial parameters can be tuned for this particular bin in the settings.py file.

      Once the fit is good enough, do not redo all fits, just fix next failed fit.

   7. One can redo any kind of fit bin by bin. For instance the MC with altSig fit (if the constraint parameters were bad in the altSig for instance)

      python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --mcSig --altSig --iBin ib

6. egm txt ouput file. Once all fits are fine, put everything in the egm format txt file

   python tnpEGM_fitter.py etc/config/setting.py  --flag myWP --sumUp

The settings file

The settings (for example settings_pho_UL2017.py) file includes all the necessary information for a given setup of fit

• General settings:

  • flags: this is the Working point in the tnpTree (pass: flagCut ; fail !flagCut). The name of the flag myWP is the one to be passed to the fitter. One can handle complex flags with a cut string (root cut string):

    flag = { 'myWP' : myWPCutString } 

  • baseOutDir: the output directory (will be created by the fitter)
  • Sample definition.
    • tnpTreeDir: the directory in the tnpTree (different for phoID, eleID, reco, hlt)
    • samplesDef: these are the main info
      • data: data ntuple
      • mcNom: nominal MC sample
      • mcAlt: MC for generator syst
      • tagSel: usually same as nominal MC + different base cuts: check the tag selection syst
    • All the samples in the samplesDef are defined in tnpSampleDef.py. (the attribute nEvts, lumi are not necessary for the fit per-se and can be omitted).

• Cuts:

  • cutBase: Define here the main cut
  • additionalCuts: can be used for cleaning cuts (or put additionalCuts = None)

• Fitting parameters: Define in this section the init parameters for the different fit, can be tuned to improve convergence.

Update PU weights

1. Pileup files have to be computed with:

   python etc/scripts/pureweight.py

   Here one has to update the name of the directory where the files will be located and the corresponding names.

2. This python uses the following: puReweighter.py. Here one nees to add the PU MC mix numbers that are available here: http://cmslxr.fnal.gov/source/SimGeneral/MixingModule/python/?v=CMSSW_9_4_0

3. One also needs to update sample names here: tnpSampleDef.py

4.The data PU distrubtions can be computed using the following instructions (similar to what is done in step1):

pileupCalc.py -i /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions17/13TeV/PromptReco/Cert_294927-306462_13TeV_PromptReco_Collisions17_JSON.txt --inputLumiJSON /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions17/13TeV/PileUp/pileup_latest.txt --calcMode true --minBiasXsec 69200 --maxPileupBin 100 --numPileupBins 100 pileup_2017_41fb.root

Other pu files for each run, like pileup_2017_RUNB.root, pileup_2017_RUNC.root etc, can be copied from previous location. The previous location of pu directory can be found in github. For example, in this version, the location is /eos/cms/store/group/phys_egamma/swmukher/tnp/ID_V2_2017/PU

5. The nvtx and rho histos are not needed because we will use the pu method (type = 0) for the reweight.

NOTE: Before using these py in order to load the needed libraires one has to run:

export  PYTHONPATH=$PYTHONPATH:/afs/cern.ch/user/s/soffi/scratch0/TEST/CMSSW-10-0-0-pre3/src/egm_tnp_analysis