First, install the NanoAOD tools:

export SCRAM_ARCH=slc6_amd64_gcc700
cmsrel CMSSW_10_3_3
cd CMSSW_10_3_3/src
cmsenv
git clone https://github.com/cms-nanoAOD/nanoAOD-tools.git PhysicsTools/NanoAODTools
scram b

Then clone this repository:

git clone https://github.com/msommerh/Zprime_to_bb Zprime_to_bb
cd Zprime_to_bb
source setupEnv.sh

Install combine according to the manual: http://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/#cc7-release-cmssw_10_2_x-recommended-version

Set the desired paths in global_paths.py

For every new shell session, do

cd CMSSW_10_3_3/src
cmsenv
cd Zprime_to_bb
source setupEnv.sh

for HTCondor submission, get the GRID certificate, find its location and put the path into global_paths.py:

voms-proxy-init --voms cms --valid 200:00
echo $(voms-proxy-info -path)

[year]: 2016, 2017, 2018; [btagging]: tight, medium, loose

create histograms for each year/masspoint of tagged and untagged jets directly fron the NanoAOD signal files:

./postprocessors/BTaggingEfficiency.py -mp

output in btag/MC_signal_histst/

evaluate the btagging efficiency in each year:

./getBTagEfficiencies.py -y 2016 2017 2018 -w [btagging] -p

output in btag/

[year]: 2016, 2017, 2018

MC signal:

./submit.py -q workday -y [year] -MC -MT signal

MC background: [MC_type]: QCD, TTbar

./submit.py -q workday -y [year] -MC -MT [MC_type] -n 1 -mn

data:

./submit.py -q workday -y [year] -n 1 -mn

single muon data (trigger efficiency study):

./submit.py -q workday -y [year] -n 1 -mn -Tr

./check_submission.sh

resubmit erroneous samples by putting the sample name in the corresponding file in resubmission/resubmit_[sample_name] and run:

./submit.py [same options as original submission (or slightly adjusted)] -rs resubmission/resubmit_[sample_name]

if the submission only crashes on a single file, identify the file nr from the stdout and resubmit via:

./submit.py [same options as original submission] -rs resubmission/resubmit_[sample_name] -rf [file_nr]

output in: global_paths.PRODUCTIONDIR

./postprocessors/skim.py

output in global_paths.WEIGHTEDDIR

trigger study:

./postprocessors/skim_SingleMuon.py

output in global_paths.SKIMMEDDIR/TriggerStudy

[year]: 2016, 2017, 2018; [MC_type]: signal, QCD, TTbar

./postprocessors/addWeights.py -y [year] -MC -MT [MC_type]
./postprocessors/addWeights.py -y [year] 

output in global_paths.SKIMMEDDIR

./postprocessors/BTaggingUncertainties.py -b [btagging]

output directly written into BTag_uncertainties.py, which is imported by samples.py

plot all relevant variable distributions:

Plot_all.sh all

output in: plots/[preselection, 1b, 2b]/

[year]: 2016, 2017, 2018

extract genParticle information from NanoAOD by running:

postprocessors/Acceptance.py -y [year] -mp

output in acceptance/

then plot acceptance as a function of mass points:

Plot_all.sh acc

plot the trigger efficiency:

Plot_all.sh trig

and plot the efficiency:

Plot_all.sh eff

output in: plots/Efficiency

[year]: 2016, 2017, 2018, run2; [btagging]: tight, medium, loose

fit on the MC signal:

./Signal_Fitter.py -y [year] -b [btagging]

before running the signal fits for the first time, one might need to load the signal model:

cd PDFs/
root -l
.L HWWLVJRooPdfs.cxx++
.q 
cd ..

fit on MC background:

./Bkg_Fitter.py -y [year] -M -b [btagging]

fit on data: (currently not advised until blinding implemented or explicitly allowed)

./Bkg_Fitter.py -y [year] -b [btagging]

output in:

plots: plots/[btagging]/[data_type]_[year]/

workspace: workspace/[btagging]/

(works independently of the actual fits)

[year]: 2016, 2017, 2018, run2; [btagging]: tight, medium, loose

MC:

./Datacards.py -M -y [year] -b [btagging]

data:

./Datacards.py -y [year] -b [btagging]

output in: datacards/[btagging]/

(for this step, you first need to go to the combine tool directory and do cmsenv)

./combineCards.sh [btagging]

for running combine on run2 with fits in separate years:

./combineCards_run2.sh [btagging]

output in: datacards/[btagging]/combined/

[year]: 2016, 2017, 2018, run2, run2c (combining the fits of the three years separately); [btagging]: tight, medium, loose

MC:

./submit_combine.py -MC -q longlunch -y [year] -b [btagging]

data: (currently not advised until blinding implemented or explicitly allowed)

./submit_combine.py -q longlunch -y [year] -b [btagging]

output in: combine/limits/[btagging]/

MC:

./limit.py -M -y [year] -b [btagging]

data: (currently not advised until blinding implemented or explicitly allowed)

./limit.py -y [year] -b [btagging]

output in: combine/plotsLimit/