run3_llp_analyzer

Class for analyzing the 2015 razor ntuples

Setup

cmsrel CMSSW_10_6_30
cd CMSSW_10_6_30/src
cmsenv
git clone git@github.com:cms-lpc-llp/run3_llp_analyzer.git run3_llp_analyzer
cd run3_llp_analyzer
make

Defining a new analysis

1. Copy analyzers/DummyAnalyzer.cc and replace each instance of "DummyAnalyzer" with the name of your desired analyzer. Modify the body of the Analyze function to define your analyzer's behavior. DO NOT need to write a header file for the analyzer class; the Makefile will generate one for you automatically.

2. Do make. This will create an executable bin/Run<name of your analyzer>. You can execute your analysis using this program directly or by calling it via the RazorRun script.

Running

After compiling,

./RazorRun <list of input files> <name of your analyzer> <options>

The "options" are the following:

-d   --isData
-f=  --outputFile=<output filename> (optional)
-n=  --optionNumber=<option number> (optional)
-l=  --optionLabel=<option Label> (optional)
-h   --help

Run the llp_analyzer

./RazorRun <list of input files> llp_MuonSystem -d=${isData} -n=${option} -f=${outputfile} -l=${tag}

• isData is yes or no
• option is currently not used
• tag is currently not used
• list of input files are stored in lists

Submit condor jobs on tier2

Before submitting jobs, make sure proxy and CMSSW environment is setup.

• run the llp_MuonSystem analyzer for data:
  • python scripts_condor/submit_condor_caltech.py
  • increment analyzer_version everytime the analyzer is being rerun

Normalizing the processed ntuples

The NormalizeNtuple macro opens a specified set of files and adds a 'weight' branch to each TTree in each file. The value of 'weight' is the same for all events in a tree and is equal to lumi * CrossSection/NEvents, where NEvents is the total number of events processed for the given dataset, and lumi is the luminosity normalized to. The cross sections can be found in the file data/xSections.dat. To run NormalizeNtuple:

./NormalizeNtuple <input file list> [lumi]

• Make sure the dataset being processed have xSections in data/xSections.dat

• Normalize the ntuples with condor job

  • python submit_normalize_caltech.py
  • the script hadd the condor jobs and normalize the ntuple to cross section for signal, only hadd is done for data

Filter good lumi events for data

https://github.com/RazorCMS/RazorCommon