FiducialXS

Framework for fiducial and differential cross section measurements using CJLST TTrees for Run 2 data.

Before using this package, please setting up Combine:

export SCRAM_ARCH=slc7_amd64_gcc700
cmsrel CMSSW_10_2_13
cd CMSSW_10_2_13/src
cmsenv
git clone https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
cd HiggsAnalysis/CombinedLimit
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit
git fetch origin
git checkout v8.1.0
scramv1 b clean; scramv1 b

Brief presentation of the codes

In this section a quick description of the codes is given, together with the ideal workflow to run the analysis. Input files of the analysis workflow are the root files generated with CJLST framework.

1. skim_MC_tree.cpp and skim_data_tree.cpp: Starting from CJLST TTrees, the branches we are interested in are selected only, both for data and signal MC
2. templates folder: Templates and normalization coefficients for the backgrounds' PDF are extracted from MC (ggZZ and qqZZ) and data (ZX)
3. coefficients folder: All the coefficients of the signal parameterization are calculated
4. fit folder: The maximum likelihood fit is performed
5. LHScans: Likelihood scans are plotted, best-fit values and the correspodnign uncertainties are calculated
6. producePlots.py: Unfolded differential distributions are plotted