Introduction

This page describe how can you run the Mono-H analyzer using the NCU tuples. Once you have the NCUGlobalTuples.root in the /store/ area then you are all set to do following steps. The code is already applying all the following re-weightings:

• pile up weights
• B-tagging scale factors
• EWK corrections for W/Z
• Top pT reweighting

Samples

One need to process following samples in order to perform the analysis, So before going to next steps make sure you have all these samples with you.

For data:

## For ICHEP dataset
/MET/Run2016B-PromptReco-v2/MINIAOD
/MET/Run2016C-PromptReco-v2/MINIAOD
/MET/Run2016D-PromptReco-v2/MINIAOD

## Include following for the full dataset in addition to ICHEP dataset 
/MET/Run2016E-PromptReco-v2/MINIAOD
/MET/Run2016F-PromptReco-v2/MINIAOD
/MET/Run2016G-PromptReco-v2/MINIAOD
/MET/Run2016H-PromptReco-v2/MINIAOD

For MC:

/ZprimeToA0hToA0chichihbb_2HDM_MZp-1000_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1000_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1000_MA0-500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1000_MA0-600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1000_MA0-700_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1000_MA0-800_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1200_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1200_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1200_MA0-500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1200_MA0-600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1200_MA0-700_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1200_MA0-800_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1400_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1400_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1400_MA0-500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1400_MA0-600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1400_MA0-700_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1400_MA0-800_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1700_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1700_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1700_MA0-500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1700_MA0-600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1700_MA0-700_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-1700_MA0-800_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2000_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2000_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2000_MA0-500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2000_MA0-600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2000_MA0-700_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2000_MA0-800_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2500_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2500_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2500_MA0-500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2500_MA0-600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2500_MA0-700_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-2500_MA0-800_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-600_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-600_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-800_MA0-300_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-800_MA0-400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-800_MA0-500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZprimeToA0hToA0chichihbb_2HDM_MZp-800_MA0-600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZJetsToNuNu_HT-100To200_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/ZJetsToNuNu_HT-600To800_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZJetsToNuNu_HT-1200To2500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZJetsToNuNu_HT-1200To2500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/ZJetsToNuNu_HT-2500ToInf_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZJetsToNuNu_HT-800To1200_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v3/MINIAODSIM
/TT_TuneCUETP8M1_13TeV-powheg-pythia8/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext3-v1/MINIAODSIM
/TT_TuneCUETP8M1_13TeV-powheg-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext4-v1/MINIAODSIM
/WJetsToLNu_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/WJetsToLNu_HT-100To200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/WJetsToLNu_HT-1200To2500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/WJetsToLNu_HT-1200To2500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/WJetsToLNu_HT-200To400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/WJetsToLNu_HT-200To400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/WJetsToLNu_HT-2500ToInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/WJetsToLNu_HT-400To600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/WJetsToLNu_HT-600To800_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/WJetsToLNu_HT-800To1200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v2/MINIAODSIM
/WJetsToLNu_HT-800To1200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/ST_s-channel_4f_leptonDecays_13TeV-amcatnlo-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ST_t-channel_antitop_4f_inclusiveDecays_13TeV-powhegV2-madspin-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ST_t-channel_antitop_4f_leptonDecays_13TeV-powheg-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ST_tW_antitop_5f_inclusiveDecays_13TeV-powheg-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ST_tW_top_5f_inclusiveDecays_13TeV-powheg-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v2/MINIAODSIM
/WW_TuneCUETP8M1_13TeV-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/WZ_TuneCUETP8M1_13TeV-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZZ_TuneCUETP8M1_13TeV-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZH_HToBB_ZToNuNu_M125_13TeV_powheg_pythia8/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v2/MINIAODSIM
/ZJetsToNuNu_HT-100To200_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZJetsToNuNu_HT-200To400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZJetsToNuNu_HT-200To400_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/ZJetsToNuNu_HT-400To600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM
/ZJetsToNuNu_HT-400To600_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM
/ZJetsToNuNu_HT-1200To2500_13TeV-madgraph/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM

Step 0: Get the code

 
## clone the master branch
git clone git@github.com:ramankhurana/MonoH.git

This also stores the histogram needed for systematics studies. You can use them to evaluate the effect on signal and bkg yourself.

Step 1: Run the skimmer

This is a very basic skimmer which will store all the branches from the NCUGlobalTuples and save them in a new rootfile after applying some cuts. This intermidiate step will change soon because this skimming is not very good in order to save space and time for final analysis. But for not due to less time is is being used which will make the offline analysis ~10-20x faster. You can direct the name of all rootfiles into a text file run it using:

python SkimTree.py 

You have to comment the following lines in the script:

inputfilename = os.environ['INPUT']
outfilename   = os.environ['OUTPUT']

And uncomment following lines:

inputfilename= 'zj100.txt'   ## this is the text file name which has path to all the input rootfiles.                                                                                                                                                        
outfilename= 'tmp.root'      ## output file name, you can change it yourself.                                                                                                                                                       

You have to repeat this for all the samples by yourself. One need to write a small script to automatise this step on lxplus.

The rootfiles as a output of the Skimmer is stored in PCNCU after merging and can be used directly for further analysis.

/data7/khurana/SkimmedTuples/V7/

Run the code for Signal Region

Step 2: Save the histograms.

This step will do the full event selection and save all the histograms needed for further analysis. This code is used for signal region and all the CRs for resolved and boosted analysis. Using the following command-line we can produce the rootfile with same name as the input rootifle. Control regions include: WJ, ZJ, TT. It can be configured using command line:

 
##For Signal Region
python MonoHBranchReader.py  -m 100.0 -M 150.0 -i NCUGlobalTuples_1.root  -a -j 0 -J 2 -l 0 -L 1 --MLow1 100.0 --MHigh1 150.0 -D HistogramsAllRegion

## For ZJ Control Region 
python MonoHBranchReader.py  -m 30.0 -M 250.0 -i NCUGlobalTuples_1.root  -a -j 0 -J 2 -l 0 -L 1  --MLow1 150.0 --MHigh1 250.0 -D HistogramsAllRegion

## For WJ Control Region 
python MonoHBranchReader.py  -m 30.0 -M 250.0 -i NCUGlobalTuples_1.root -a -j 1 -J 2 -l 1 -L 2  --MLow1 30.0 --MHigh1 250.0 -D HistogramsAllRegion

## For TT Control Region
python MonoHBranchReader.py  -m 30.0 -M 250.0 -i NCUGlobalTuples_1.root  -a -j 2 -J 10 -l 1 -L 2  --MLow1 30.0 --MHigh1 250.0 -D HistogramsAllRegion

As it is clear from the commands above that this can apply and change the cuts via command line on following variables:

• Higgs boson mass: You have four variables to define the boundary of the Higgs mass window, -m, -M, --MLow and --MHigh. -m and --MLow can have same values. -M and -MHigh can have same values. Two values are needed in order to construct the boundraies for mass side-bands, e.g. 30-100 GeV and 150-250 GeV.

• Additional Leptons: -l for minimum number of leptons. -L for maximum number of leptons. Inequalities is following the rule: -l <= leptons < -L

• Additional jets: -j for minimum number of jets. -J for maximum number of jets. Inequalities is following the rule: -j <= jets < -J

• analysis mode: -a option is to run the code in analysis mode. This will run the code in both boosted and resolved mode taking care of the orthogonality of the evens.

One need to write a small python script to run this command for all the skimmed samples automatically. I have a macro to do is using condor not for lxplus. I can help with example/hints to implement this.

Note: The script to run on all the Merged Skimmed files is now ready and can be used using following command:

python PrepareistogramsAllRegions.py

It will run on all the files for all the regions (SR and CRs). This will take about 2 hrs to run on one region so Its better if one can setup lxplus batch jobs for this. With ~270 jobs one can get all the histograms in less than one hour (generally including the wait time because individual job is short).

Step 3: Final Results

Final results can be produced using MakeShapesAndStack.py script which uses Utils.py as a source. You need to make sure that Utils.py is always updated and all the information is correct.

• Main information to be saved in the Utils.py (for now) is file name, cross-section. In case of more than one file for a process choose same order for file and cross-section value.
• Fill color, fill style ,line color-width-style, label, plot status (not used at this moment) are also need to be provided by used in case you want to change them w.r.t the default values set by me.
• weight is stored as 1.0 by default and later on setweights function will save the appropriate weight factors.
• You can take a look at the Utils.py to familiarise yourself. In the mean time i will update the following instructions.

Step 3(a): Histogram re-weighting and saving in root files.

This step will create the histograms for each background and signal and data with proper normalisation and save it in the rootfile and also create a text file with yield +- error in it. This can be used to run for all the regions in one time or edited in the __main() function to run only one or two regions. The script can be run using:

 
python MakeShapesAndStack.py -s

Step 3(b): Prepare text files with normalised event yield using the re-weighted histograms.

done in previous step using

 
python MakeShapesAndStack.py -s

Step 3(c): Prepare data cards using text files.

Before running this step you have to move the files produced in previous step into a new directory which will be used as input for next step.

 
source filemover.sh

This will move files into a directory, "AllRegions" In case you decide to change this name then you will have to make the change in all the following steps also. So be careful here.

You can now make the cards for each of the region using:

 
python MakeShapesAndStack.py -m

All the datacards will be saved in the new directory: "DataCards_AllRegions" and rootfile needed for the datacards is also present in the same directory.

For following steps you need to have the Higgs combination package installed in your cmssw area. You can follow the instructions from Higgs Combination Twiki

You can now combine the cards using:

 
python MakeShapesAndStack.py -c

You can add the bin by bin uncertainty histograms in the same rootfile using the shell script (this will become part of python soon). Don't worry if this doesn't work for you at this moment.

 
source binbybin.sh

Step 3(d): Prepare pdf/png/root/macro for each variables for signal region, WJ, ZJ, TT in boosted and resolved regime.

COMING SOON

Step 4: Limits

Step 4(a): Prepare workspace for Mono-H bb analysis using the datacards and rootfiles with normalised histograms.

COMING SOON

Step 4(b): Run the limits on full mass grid

You can run the asimov limits using the same script which is used to combine the cards using:

python MakeShapesAndStack.py -r

After unblinding data you can run the limits using data with same script:

python CombineDataCards.py DataCards_AllRegions runlimit obs

Step 4(c): Plot the 1D limits

Plot the1D limit using

source  RunLimit.sh DataCards_AllRegions myfirsttrial

where, RunLimit.sh is the script which is running plot_Asymptotic_Limit.C inside it. DataCards_AllRegions is the dirname where you have kept the rootifle with limit trees. myfirsttrial is the post-fix name of the png file of the plot. A base string is already given in the macro so in order to distinguish between differnt version of plots this post-fix will help.

Step 4(d): Plots the 2D limits

Step 5: For Mono-H Combination

The instructions will appear soon.

Some important links:

For Bin width optimisation

-- RamanKhurana - 2016-09-26