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README_caloscouting_analysis.md

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Instructions to run Calo Scouting Dijet Resonance Search from start to finish.

Set up CMSSW/DijetRootTreeAnalyzer/combine

  1. Set up CMSSW/DijetRootTreeAnalyzer/combine

    $ cmsrel CMSSW_7_4_14
$ cd CMSSW_7_4_14/src
$ cmsenv
$ git clone https://github.com/CMSDIJET/DijetRootTreeAnalyzer CMSDIJET/DijetRootTreeAnalyzer
$ git clone -b dijetpdf_74X https://github.com/RazorCMS/HiggsAnalysis-CombinedLimit HiggsAnalysis/CombinedLimit
$ cd HiggsAnalysis/CombinedLimit
$ scram b -j 4
$ cd $CMSSW_BASE/CMSDIJET/DijetRootTreeAnalyzer

Getting mjj histogram for fit (not necessary because histogram is in repo)

  1. Add files from EOS together

    $ python/AddFiles.py -l lists/CaloScoutingHT_2016_JEC_CaloL1L2L3_PFL2L3Residual_NewBiasCorrected_Golden12910pb_20160723_003113_reduced_skim.txt  -o inputs/data_CaloScoutingHT_Run2016BCD_NewBiasCorrected_Golden12910pb_CaloDijet2016.root

Copy CaloScouting signal resonance files

  1. Copy CaloScouting signal resonance files

    $ wget https://github.com/CMSDIJET/DijetShapeInterpolator/raw/master/ResonanceShapes_gg_13TeV_CaloScouting_Spring16.root -P inputs/
$ wget https://github.com/CMSDIJET/DijetShapeInterpolator/raw/master/ResonanceShapes_gg_13TeV_CaloScouting_Spring16_JERUP.root -P inputs/
$ wget https://github.com/CMSDIJET/DijetShapeInterpolator/raw/master/ResonanceShapes_gg_13TeV_CaloScouting_Spring16_JERDOWN.root -P inputs/
$ wget https://github.com/CMSDIJET/DijetShapeInterpolator/raw/master/ResonanceShapes_gg_13TeV_CaloScouting_Spring16_JESUP.root -P inputs/
$ wget https://github.com/CMSDIJET/DijetShapeInterpolator/raw/master/ResonanceShapes_gg_13TeV_CaloScouting_Spring16_JESDOWN.root -P inputs/

Fitting (for limits, skip this section)

  1. Perform an unblinded, background-only, full region fit (with signal shapes plotted)

    $ mkdir -p fits_2016_07_24/
$ mkdir -p fits_2016_07_24/CaloDijet2016_Full
$ python python/BinnedFit.py -c config/dijet.config -l 12910 --mass 750_1200_1600 -m gg_qg_qq --xsec 9.5_8.2e-1_2.2e-1 -s inputs/ResonanceShapes_gg_13TeV_CaloScouting_Spring16.root inputs/data_CaloScoutingHT_Run2016BCD_NewBiasCorrectedFlat_Golden12910pb_CaloDijet2016.root -b CaloDijet2016 -d fits_2016_07_24/CaloDijet2016_Full/ --fit-spectrum

  2. Run and fit 1000 toys and plot GOF for unblinded fit

    $ python python/RunToys.py -b CaloDijet2016 --freq -c config/dijet.config --lumi 12910 --fit-region Full  -d fits_2016_07_24/CaloDijet2016_Full/ -i fits_2016_07_24/CaloDijet2016_Full/DijetFitResults_CaloDijet2016.root  -t 1000 -s 0
$ python python/PlotGOF.py -b CaloDijet2016 -c config/dijet.config -d fits_2016_07_24/CaloDijet2016_Full/ -t fits_2016_07_24/CaloDijet2016_Full/toys_Freq_s0_CaloDijet2016.root -l 12910 --data

Setting limits

  1. Run combine to produce expected and observed limits for gg resonance mass range [500, 1600] in 50 GeV steps

    $ mkdir -p cards_gg_freq
$ python python/RunCombine.py -m gg -d cards_gg_freq/ --mass range\(500,1650,50\) -c config/dijet.config -i inputs/DijetFitResults.root -b CaloDijet2016 --rMax 20 --xsec 10 -l 12.910

  2. Convert combine output files and plot 1D limit

    $ python python/GetCombine.py -d cards_gg_freq/ -m gg --mass range\(500,1650,50\) -b CaloDijet2016 --xsec 10 -l 12.910
$ python python/Plot1DLimit.py -d cards_gg_freq/ -m gg -b CaloDijet2016 -l 12.910 --massMin 600 --massMax 1600 --xsecMin 1e-3 --xsecMax 1e5

Bias studies with 4-parameter modified exponential

  1. Run combine-based bias studies with 1000 toys for r = 1 and 10 pb cross section using 4-parameter modified exponential for generation and 4-parameter dijet functin for fitting (defined in confif/dijet_bias.config)

    $ mkdir signal_bias/
$ python python/RunBias.py -c config/dijet_bias.config --mass 750 -m gg -d signal_bias/ -r 1 -l 12.910 --xsec 10 -t 1000 --gen-pdf modexp  --fit-pdf fourparam

  2. Make plots of bias

    $ python python/PlotBias.py -c config/dijet_bias.config --mass 750 -m gg -d signal_bias/ -r 1 -l 12.910 --xsec 10 -t 1000 --gen-pdf modexp  --fit-pdf fourparam