Code to perform fits, bias studies, compute limits and significances
First, we need to set up out CMSSW working area and check out and compile the HiggsAnalysis/CombinedLimit package:
setenv SCRAM_ARCH slc6_amd64_gcc491
cmsrel CMSSW_7_4_7
cd CMSSW_7_4_7/src
cmsenv
git clone -b v6.2.0 --depth 1 https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
scram b clean
scram b -j8
Next, we will also need some dijet specific code and we will put it in the test/ subdirectory of our CMSSW working area:
cd ../test
git clone git://github.com/CMSDIJET/DijetShapeInterpolator.git DijetShapeInterpolator
git clone git://github.com/CMSDIJET/StatisticalTools.git StatisticalTools
If you are also interested in computing limits using theta (http://theta-framework.org/), you will also need to check out its code and compile it:
svn co https://ekptrac.physik.uni-karlsruhe.de/public/theta/tags/testing theta
cd theta/
make
cd ..
Note that this feature is still experimental.
All of the above steps need to be done only once.
NOTE: Branches containing any non-public data and/or results are stored in a private instance of the StatisticalTools repository hosted by CERN GitLab. Here are the steps you should follow to get private branches:
cd StatisticalTools
git remote add cms-internal ssh://git@gitlab.cern.ch:7999/CMSDIJET/StatisticalTools.git
git fetch cms-internal
at which point you can either update your current branch (in case it contains any private data)
git pull --ff-only cms-internal <current_branch>
or can switch to another (possibly private) branch
git checkout <new_branch>
where <new_branch> is the name of the branch to which you are switching.
Alternatively, you can start by directly cloning from the private repository
git clone -b <branch> ssh://git@gitlab.cern.ch:7999/CMSDIJET/StatisticalTools.git StatisticalTools
Before we can compute limits or significances, we need signal resonance shapes. Since we'll be using finely binned resonance shapes required by combine and RooFit, and given the number of signal mass points, the ROOT files storing resonance shapes will be several MB in size. So in order not to bloat our repositories with many MBs of binary ROOT files, the resonance shapes will not be stored in the DijetShapeInterpolator or StatisticalTools repositories but will instead be produced using scripts and information stored in the DijetShapeInterpolator repository. Nevertheless, it is hard to completely avoid storing binary ROOT files in the repository so in some cases we will still do it (e.g. data dijet spectrum). Generally, this practice should be limited to ROOT files that are small (not more than a few hundred kB) and/or are not expected to change frequently.
To produce the resonance shapes, go to the DijetShapeInterpolator package
cd ../
cd DijetShapeInterpolator
and run the following commands:
./getResonanceShapes.py -i inputs/input_shapes_gg_13TeV_PU30_Spring15.py -f gg --fineBinning --massrange 500 9000 100 -o ResonanceShapes_gg_13TeV_PU30_Spring15.root
./getResonanceShapes.py -i inputs/input_shapes_qg_13TeV_PU30_Spring15.py -f qg --fineBinning --massrange 500 9000 100 -o ResonanceShapes_qg_13TeV_PU30_Spring15.root
./getResonanceShapes.py -i inputs/input_shapes_qq_13TeV_Phys14Spring15Mix.py -f qq --fineBinning --massrange 500 9000 100 -o ResonanceShapes_qq_13TeV_Phys14Spring15Mix.root
This will produce gg, qg, and qq resonance shapes. For more command-line options, run
./getResonanceShapes.py -h
Finally, move the produced resonance shapes to the StatisticalTools package:
mv ResonanceShapes_gg_13TeV_PU30_Spring15.root ResonanceShapes_qg_13TeV_PU30_Spring15.root ResonanceShapes_qq_13TeV_Phys14Spring15Mix.root ../StatisticalTools/inputs/
cd ../StatisticalTools
Another essential ingredient for the statistical analysis are datacards and corresponding RooFit workspaces. Here again, we don't necessarily want to store all of these files in the repository since they can be easily remade using scripts available in the repository. Run the following commands to produce datacards for gg, qg, and qq resonances:
./scripts/createDatacards.py --inputData inputs/histo_data_mjj_Run2015D_silverJSON_2445pb-1_JEC_Summer15_25nsV6_clean.root --dataHistname h_dat --inputSig inputs/ResonanceShapes_gg_13TeV_PU30_Spring15.root -f gg -o datacards -l 2445 --lumiUnc 0.05 --jesUnc 0.02 --jerUnc 0.1 --massrange 1500 7200 100 --runFit --p1 7.46697e+00 --p2 6.37033e+00 --p3 2.14709e-01 --massMin 1181 --massMax 10430 --fitStrategy 2
./scripts/createDatacards.py --inputData inputs/histo_data_mjj_Run2015D_silverJSON_2445pb-1_JEC_Summer15_25nsV6_clean.root --dataHistname h_dat --inputSig inputs/ResonanceShapes_qg_13TeV_PU30_Spring15.root -f qg -o datacards -l 2445 --lumiUnc 0.05 --jesUnc 0.02 --jerUnc 0.1 --massrange 1500 7200 100 --runFit --p1 7.46697e+00 --p2 6.37033e+00 --p3 2.14709e-01 --massMin 1181 --massMax 10430 --fitStrategy 2
./scripts/createDatacards.py --inputData inputs/histo_data_mjj_Run2015D_silverJSON_2445pb-1_JEC_Summer15_25nsV6_clean.root --dataHistname h_dat --inputSig inputs/ResonanceShapes_qq_13TeV_Phys14Spring15Mix.root -f qq -o datacards -l 2445 --lumiUnc 0.05 --jesUnc 0.02 --jerUnc 0.1 --massrange 1500 7200 100 --runFit --p1 7.46697e+00 --p2 6.37033e+00 --p3 2.14709e-01 --massMin 1181 --massMax 10430 --fitStrategy 2
For more command-line options, run
./scripts/createDatacards.py -h
For Bayesian limits a separate set of datacards and RooFit workspaces is needed if the background systematics will be taken into account because in that case the background parameters need to be decorrelated (the --decoBkg option):
./scripts/createDatacards.py --inputData inputs/histo_data_mjj_Run2015D_silverJSON_2445pb-1_JEC_Summer15_25nsV6_clean.root --dataHistname h_dat --inputSig inputs/ResonanceShapes_gg_13TeV_PU30_Spring15.root -f gg -o datacards_decoBkg -l 2445 --lumiUnc 0.05 --jesUnc 0.02 --jerUnc 0.1 --massrange 1500 7200 100 --runFit --p1 7.46697e+00 --p2 6.37033e+00 --p3 2.14709e-01 --massMin 1181 --massMax 10430 --fitStrategy 2 --decoBkg
./scripts/createDatacards.py --inputData inputs/histo_data_mjj_Run2015D_silverJSON_2445pb-1_JEC_Summer15_25nsV6_clean.root --dataHistname h_dat --inputSig inputs/ResonanceShapes_qg_13TeV_PU30_Spring15.root -f qg -o datacards_decoBkg -l 2445 --lumiUnc 0.05 --jesUnc 0.02 --jerUnc 0.1 --massrange 1500 7200 100 --runFit --p1 7.46697e+00 --p2 6.37033e+00 --p3 2.14709e-01 --massMin 1181 --massMax 10430 --fitStrategy 2 --decoBkg
./scripts/createDatacards.py --inputData inputs/histo_data_mjj_Run2015D_silverJSON_2445pb-1_JEC_Summer15_25nsV6_clean.root --dataHistname h_dat --inputSig inputs/ResonanceShapes_qq_13TeV_Phys14Spring15Mix.root -f qq -o datacards_decoBkg -l 2445 --lumiUnc 0.05 --jesUnc 0.02 --jerUnc 0.1 --massrange 1500 7200 100 --runFit --p1 7.46697e+00 --p2 6.37033e+00 --p3 2.14709e-01 --massMin 1181 --massMax 10430 --fitStrategy 2 --decoBkg
We can now run combine to compute the limits. In the following examples the Asymptotic CLS method is used:
./scripts/runCombine.py -M Asymptotic -d datacards -f gg --massrange 1500 7200 100
./scripts/runCombine.py -M Asymptotic -d datacards -f qg --massrange 1500 7200 100
./scripts/runCombine.py -M Asymptotic -d datacards -f qq --massrange 1500 7200 100
To get limits without any systematics included, add the --noSyst and --freezeNuisances shapeBkg_background_bin1__norm,p1,p2,p3 options (the second option is specifically needed for the background systematics not handled by the --noSyst option).
For more command-line options, run
./scripts/runCombine.py -h
In particular, note the --condor option which allows you to process all mass points in parallel using the Condor batch system (currently only Condor at FNAL is supported).
To produce the final limit plots, run
./scripts/plotLimits.py -M Asymptotic -l logs -f gg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotLimits.py -M Asymptotic -l logs -f qg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotLimits.py -M Asymptotic -l logs -f qq --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
For more command-line options, run
./scripts/plotLimits.py -h
If you are only interested in producing or modifying plots using already computed results, you can use the -r (--results_file) instead of the -l (--logs_path) option as in the following example:
./scripts/plotLimits.py -M Asymptotic -r results/limits_Asymptotic_gg_Run2_13TeV_DATA_2445_invpb.py -f gg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps
To repeat the same procedure for Bayesian limits (only observed limits for now), run the following set of commands
./scripts/runCombine.py -M MarkovChainMC -d datacards_decoBkg -f gg --massrange 1500 7200 100
./scripts/runCombine.py -M MarkovChainMC -d datacards_decoBkg -f qg --massrange 1500 7200 100
./scripts/runCombine.py -M MarkovChainMC -d datacards_decoBkg -f qq --massrange 1500 7200 100
./scripts/plotLimits.py -M MarkovChainMC -l logs -f gg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotLimits.py -M MarkovChainMC -l logs -f qg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotLimits.py -M MarkovChainMC -l logs -f qq --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
To get Bayesian limits without any systematics included, add the --noSyst and --freezeNuisances shapeBkg_background_bin1__norm,deco_eig1,deco_eig2,deco_eig3 options (the second option is specifically needed for the background systematics not handled by the --noSyst option). However, in this specific case, to speed up the computation, you can use the same datacards as for Asymptotic limits and add the --noSyst and --freezeNuisances shapeBkg_background_bin1__norm,p1,p2,p3 options.
As an additional cross-check, Bayesian limits can also be produced using theta. Since theta requires histograms as input, you first need to produce them. This can be accomplished by simply adding the --theta option to the above commands for making datacards. Note that in addition to the input histograms, the corresponding Python analysis files will be created as well that define the statistical model, quantities to be computed, etc. To compute the limits, run
./scripts/runTheta.py -d datacards_decoBkg -f gg --massrange 1500 7200 100
To produce the final limit plot, run
./scripts/plotLimits.py -M theta -l logs -f gg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
For the significance calculation we again use the runCombine.py script as in the following examples (note the --signif option):
./scripts/runCombine.py -M ProfileLikelihood --signif -d datacards -f gg --massrange 1500 7200 100
./scripts/runCombine.py -M ProfileLikelihood --signif -d datacards -f qg --massrange 1500 7200 100
./scripts/runCombine.py -M ProfileLikelihood --signif -d datacards -f qq --massrange 1500 7200 100
To produce the final significance plots, run
./scripts/plotSignificance.py -M ProfileLikelihood -l logs -f gg --massrange 1500 7200 100 --sigRange 2.5 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotSignificance.py -M ProfileLikelihood -l logs -f qg --massrange 1500 7200 100 --sigRange 2.5 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotSignificance.py -M ProfileLikelihood -l logs -f qq --massrange 1500 7200 100 --sigRange 2.5 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
For more command-line options, run
./scripts/plotSignificance.py -h
If you are only interested in producing or modifying plots using already computed results, you can use the -r (--results_file) instead of the -l (--logs_path) option as in the following example:
./scripts/plotSignificance.py -M ProfileLikelihood -r results/significance_ProfileLikelihood_gg_Run2_13TeV_DATA_2445_invpb.py -f gg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps
The runCombine.py script can also be used to extract the best-fit signal cross sections as in the following examples:
./scripts/runCombine.py -M MaxLikelihoodFit -d datacards -f gg --rMin -50 --rMax 50 --massrange 1500 7200 100
./scripts/runCombine.py -M MaxLikelihoodFit -d datacards -f qg --rMin -50 --rMax 50 --massrange 1500 7200 100
./scripts/runCombine.py -M MaxLikelihoodFit -d datacards -f qq --rMin -50 --rMax 50 --massrange 1500 7200 100
To produce the final cross section plots, run
./scripts/plotSignalXSec.py -l logs -f gg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotSignalXSec.py -l logs -f qg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
./scripts/plotSignalXSec.py -l logs -f qq --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps --printResults
For more command-line options, run
./scripts/plotSignalXSec.py -h
If you are only interested in producing or modifying plots using already computed results, you can use the -r (--results_file) instead of the -l (--logs_path) option as in the following example:
./scripts/plotSignalXSec.py -r results/signal_xs_MaxLikelihoodFit_gg_Run2_13TeV_DATA_2445_invpb.py -f gg --massrange 1500 7200 100 --extraText Preliminary --lumi_sqrtS="2.4 fb^{-1} (13 TeV)" --postfix Run2_13TeV_DATA_2445_invpb --fileFormat eps