Repository for analysis code
Recipe for 76X (temp fixes for pileup jet id):
cmsrel CMSSW_7_6_4
cd CMSSW_7_6_4/src
cmsenv
git cms-init
#echo /CommonTools/PileupAlgos/ > .git/info/sparse-checkout
#echo /CommonTools/Utils/ >> .git/info/sparse-checkout
#echo /JetMETCorrections/Configuration/ >> .git/info/sparse-checkout
#echo /JetMETCorrections/Modules/ >> .git/info/sparse-checkout
#echo /JetMETCorrections/Type1MET/ >> .git/info/sparse-checkout
#echo /PhysicsTools/PatAlgos/ >> .git/info/sparse-checkout
#echo /PhysicsTools/PatUtils/ >> .git/info/sparse-checkout
#echo /RecoMET/METAlgorithms/ >> .git/info/sparse-checkout
#echo /RecoMET/METProducers/ >> .git/info/sparse-checkout
git cms-merge-topic cms-met:metTool76X
git cms-addpkg RecoJets/JetProducers
git-cms-merge-topic jbrands:pileupJetId76X
cd RecoJets/JetProducers/data/
wget https://github.com/jbrands/RecoJets-JetProducers/raw/3dad903ed25d025f68be94d6f781ca957d6f86ac/pileupJetId_76x_Eta0to2p5_BDT.weights.xml.gz
wget https://github.com/jbrands/RecoJets-JetProducers/raw/3dad903ed25d025f68be94d6f781ca957d6f86ac/pileupJetId_76x_Eta2p5to2p75_BDT.weights.xml.gz
wget https://github.com/jbrands/RecoJets-JetProducers/raw/3dad903ed25d025f68be94d6f781ca957d6f86ac/pileupJetId_76x_Eta2p75to3_BDT.weights.xml.gz
wget https://github.com/jbrands/RecoJets-JetProducers/raw/3dad903ed25d025f68be94d6f781ca957d6f86ac/pileupJetId_76x_Eta3to5_BDT.weights.xml.gz
cd ../../..
git cms-merge-topic -u matteosan1:smearer_76X
git clone git@github.com:avartak/AnalysisCode.git -b Raffaele_76X
git clone https://github.com/rfriese/RecoMET-METPUSubtraction data -b 74X-13TeV-Summer15-July2015
rm -rf data/.git
rm JetMETCorrections/Modules/src/SealModule.cc
How to Run the ntuple production (for analysis):
cd CMSSW_7_6_4/src/AnalysisCode/MonoXAnalysis/test
cmsRun tree.py <list of options>
Example:
cmsRun tree.py isMC=True
Options:
isMC : set to True when running on simulated events
isFastSIM : set to True when running on fast sim miniAOD (still temp and relying on Emanuele miniAOD)
filterHighMETEvents : apply metCut GeV seletion on recoil/met (mumet or elmet or phmet or met)
metCut : met threshold
filterOnHLT : require the OR of all the trigger path definied in the MonoJetTreeMaker (beginRun)
usePrivateSQliteJEC : use a local db file for jet energy correction
usePrivateSQliteJER : use a local db file for jet energy smearings (by default true since no records in existing GT)
applyL2L3Residuals : apply or not L2L3 residuals on data
addPileupJetID : re-run the pileup-jet id on AK4PFchs jets (new tune for 76X)
addQGLikelihood : add QGL value for AK4PFchs and AK4PFPuppi in case
addPuppiJets : dump Puppi jet info in the MonoJetTreeMaker and add all the related modules in the process
addPuppiMET : dump Puppi met info in the MonoJetTreeMaker and add all the related modules in the process
addMVAMet : add the MVA met (still not working in 76X)
useMiniAODMet : set to True when using miniAOD computed met and related systematics
addMETSystematics : dump MET systematic variations in the tree and, when useMiniAODMet = False, re-evaluate thorugh METSystematicProducer (for PUPPI too)
useOfficialMETSystematics : use the official MET tool for systematics and not the private code
addSubstructureCHS : compute all substructure info for PFCHS jets (different parameters can be changed directly in tree.py)
addSubstructurePuppi: compute all substructure info for PFPuppi jets
processName : name of the cms.Process
miniAODProcess : name of the cms.Process used in miniAOD production --> useful for MET filters..etc
outputFileName : name of the output root file from TFileService
globalTag : GT to be used--> default for MC = 76X_mcRun2_asymptotic_RunIIFall15DR76_v1, default for DATA = 76X_dataRun2_16Dec2015_v0
JECEra : JEC era string
useLHEWeights : dump LHE weights and info (only for MC samples)
addQCDPDFWeights : dump scalar and PDF variations (only for MC samples)
addGenParticles : dump gen level info like W/Z boson and related decaying particles, gen photons, top and anti-top
isSignalSample : dump gen level info for DM particles and mediator, as well as reading masses from the LHE header
crossSection : fix the cross section branch in fb-1 from an external value
dropAnalyzerDumpEDM : to debug, avoid analyzers and dump edm collections produced in the event
nThreads : number of threads
isCrab : to be used to handle correctly local files with Crab
Some useful information:
Global Tag:
https://twiki.cern.ch/twiki/bin/view/CMSPublic/SWGuideFrontierConditions
Recipe for pileup-reweight:
https://twiki.cern.ch/twiki/bin/view/CMS/PileupJSONFileforData#2015_Pileup_JSON_Files
Example:
pileupCalc.py -i Cert_246908-260627_13TeV_PromptReco_Collisions15_25ns_JSON_v2.txt --inputLumiJSON pileup_JSON_11-19-2015.txt --calcMode true --minBiasXsec 69000 --maxPileupBin 50 --numPileupBins 50 Cert_246908-260627_13TeV_PromptReco_Collisions15_25ns_JSON_v2.root
MiniAOD content:
https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookMiniAOD2015
Recipe for muon ID:
https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2
Recipe for electron ID (cut based):
https://twiki.cern.ch/twiki/bin/view/CMS/CutBasedElectronIdentificationRun2
Recipe for photon ID (cut based):
https://twiki.cern.ch/twiki/bin/view/CMS/CutBasedPhotonIdentificationRun2
Recipe for jet ID:
https://twiki.cern.ch/twiki/bin/view/CMS/JetID
Recipe for pileup jet ID:
https://twiki.cern.ch/twiki/bin/view/CMS/PileupJetID
Recipe for Puppi and Puppi MET:
https://twiki.cern.ch/twiki/bin/viewauth/CMS/PUPPI
Recipe for Quark-Gluon likelihood ID:
https://twiki.cern.ch/twiki/bin/view/CMS/QuarkGluonLikelihood
Recipe for B-tagging and scale factor:
https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation
Recipe for V-tagging:
https://twiki.cern.ch/twiki/bin/view/CMS/JetWtagging
Recipe for DoubleB-tagging:
https://twiki.cern.ch/twiki/bin/view/CMS/Hbbtagging
Recipe for JEC variations:
https://twiki.cern.ch/twiki/bin/view/CMS/JECDataMC
Recipe for JER smearings:
https://twiki.cern.ch/twiki/bin/view/CMS/JetResolution
Recipe for MET filters:
https://twiki.cern.ch/twiki/bin/view/CMS/MissingETOptionalFiltersRun2
Recipe for MVA MET:
https://twiki.cern.ch/twiki/bin/viewauth/CMS/MVAMet
Recipe for Recoil Correction:
https://twiki.cern.ch/twiki/bin/view/CMS/MissingETRun2RecoilCorrection
Please have a look to the following twiki: https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideHiggsAnalysisCombinedLimit#ROOT6_SLC6_release_CMSSW_7_4_X
Python multicrab script can be used to submit on multiple dataset in one shot
cd AnalysisCode/MonoXAnalysis/test/crab
python <multicrab.py> <multicrab cfg>
Examples on how to setup the python for data and MC are crab/multicrab_MC_76X.py crab/multicrab_DATA_76X.py:
pyCfgParams : list of all the parameters to be given to the cmsRun application when the job is executed on the grid
config.JobType.numCores : to allow multi-thread process .. the value should be the same of nThread
All the other parameters are the standard crab3 ones: https://twiki.cern.ch/twiki/bin/view/CMSPublic/CRAB3ConfigurationFile
The config file which defines the dataset and additional parmaters for the cmsRun application can be found in SampleList_DATA_76X and SampleList_MC_76X.
samples[<name of crab di>] = [<dataset name>, [<additional parameters>]]
Example:
samples['ZJetsToNuNu_HT-100To200'] = ['/ZJetsToNuNu_HT-100To200_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM',
['useLHEWeights=True','addQCDPDFWeights=True','isSignalSample=False','addGenParticles=True','crossSection=280.5']]
The filter step macro allows to reduce the event content of the trees produced by the MonoJetTreeMaker, according to a set of predifined selections.
To run the macro:
cd AnalysisCode/MonoXAnalysis/;
root -l ;
.L fiters.C+
sigfilter(...), zmmfilter(....), zeefilter(...), wmnfilter(...), wenfilter(...), gamfilter(...), topmufilter(...), topelfilter(...)
These function are used to select signal like events, Z->mm, Z->ee, W->mnu, W->enu, gamma+jets, semi-leptonic ttbar muon, semi-leptionc ttbar electron
Basic options:
--inputFileName : name of the input file or name of the crab directory in which different files are locate (TChain is used in this case)
--outputFileName : output file .. the code can take a list of files in input but will produce a single output
--isMC : flag to indicate data or MC
--applyBTagWeights : calculates b-tag scale factors and related uncertainties
--isInputDirectory : specify if the inputFileName is a single file or a directory
--isEOS : in case of a directoy this indicates if it is located in EOS or in a local directory
--xsType: 0 means use the xsec branch stored in the origin MonoJetTreeMaker tree, 1 valuate the xs as sumwgt/Nevents (useful for powheg+minlo mono-jet samples), 2 means take the xs from LHE header stored in the genTree.
--storeGenTree : save the whole gen tree in the output
--isSinglePhoton: options available only for zeefilter, wenfilter and topelfilter allows to run on single photon dataset keeping events triggered by singlephoton but not by single electron triggers .. aim is to increase the statistics at high pt.
To run the filter step on a crab output:
python scripts/runFilters.py -b <options>
Options:
--inputDIR : input directory path
--outputDIR : output directory path
--filterName = "sigfilter",....... "all" => when you want to run all the filters (one filter = one job)
--calculateXSfromSW : to calculate xs from sum of weights
--calculateXSfromLHE : take xs from LHE header
--isMC
--applyBTagSF
--storeGenTree
--isSinglePhoton
--isCrabDirectory : indicates if the directory has been created by crab (use a recursive find method to make the file list for the TChain)
--isOnEOS
--batchMode : means run the filter.C code on lxbatch
--jobDIR : direcotry where all the jobs sh are created
--queque <string>
--submit : to submit while created
Example:
python scripts/runFilters.py --inputDIR /store/group/upgrade/delphes/VBS_SS/Production-03-04-2016//WWToLNuQQ_13TeV-powheg --outputDIR /store/group/upgrade/delphes/VBS_SS/Production-03-04-2016/SkimmedProduction-12-05-2016/ --filterName all --jobDIR JOB --queque 1nd --batchMode --submit --isOnEOS --isMC --applyBTagSF --isCrabDirectory
If you want to run in one shot filters on a set of outputs produced by crab for different datasets you can use:
python scripts/submitFilters.py <options>
Options:
--inputDIR : mother crab directory, typically located on EOS
--outputDIR : output directory .. typically located on EOS
<many options in common with scripts/runFilters.py>
--grepName <name 1>,<name 2> ..: to look only at specific subdir which name contains all the string in the list
--skipName <name 1>,<name 2> ..: to skip the sub-dir which name contains one of the string in the list
Example:
python scripts/submitFilters.py --inputDIR /store/group/upgrade/delphes/VBS_SS/Production-03-04-2016/ --outputDIR /store/group/upgrade/delphes/VBS_SS/Production-03-04-2016/SkimmedProduction-12-05-2016/ --filterName all --isMC --applyBTagSF --skipName MET,SinglePhoton,SingleEle --jobDIR JOB --queque 1nd
Once all the filtered files are available in some directory (typically on EOS), a script can be used to copy all or some of them to a local machine, where the last stages of the analysis are done:
python scripts/files/copyAllDirectoriesEOS.py <options>
Options:
--inputDIR : mother direcotry where all the filtered files are located
--outputDIR : local folder on a local machine
--toEOS: in case the copy is not from EOS to a local machine but from a local machine to EOS
--grepName name1,name2... : copy only a set of sub-dirs
--skipName name1.name2... : skip some sub-dirs
Example:
python scripts/files/copyAllDirectoriesEOS.py --inputDIR /store/group/upgrade/delphes/VBS_SS/Production-03-04-2016/SkimmedProduction-12-05-2016/ --outputDIR /home/rgerosa/test/ --grepName Axial,1000
To copy only files belonging to a single directory each time, please use:
python scripts/files/copyFilesEOS.py <options>
Once all the files are copied to a local machine, is a good thing to arrange them in common directories to run the last steps of the analysis. As example, if you have for Mono-jet Vector production 100 mass points, instead of keeping 100 directories, you can merge all the filter directories (sigfilter, zmmfilter..etc) into a common one. The output root file already reflects the name of the sample so there is no need of having indipendent directories (just the way in which crab is setup).
python scripts/files/mergeDirectories.py <options>
Options: --inputDIR : mother directory that containes all the sub-directories that you wanna merge --outputDIR : name of the output common directory --grepName name1,name2... : merge only a set of sub-dirs --skipName name1.name2... : skip some sub-dirs
Example: python scripts/files/mergeDirectories.py --inputDIR /home/rgerosa/test/ --outputDIR /home/rgerosa/test/DMV_Axial/ --grepName Axial
python scripts/files/mergeFile.py <options>
Options:
--inputDIR : directory that contains all the files to be merged
--outputName : name of the single root file created
After the filter step, once all the files are copied to a local machine and correctly organized in directories, these are the useful codes to run the analysis:
cd AnalysisCode/MonoXAnalysis/macros
macros/histoUtils.h : used to define the binning of each 1D observables as std::vector<float>. In addtion there are function usefult to smooth empty bins, make the average of two histograms, fix shape uncertainties, fix empty bins for combine and generate evenvelope given a set of input histograms
macros/histoUtils2D.h: same thing for 2D histograms .. this time there are also function to perform rolling and un-rolling (useful for a 2D analysis)
macros/makehist.h: function that makes the loop on the events, apply selections and weights. Some external files are useful here and located in the AnalysisCode/MonoXAnalysis/data directory. baseInputTreePath is a string which indicated the base directory where all the input root files are located.
macros/makeCorrHistograms.C : set of functions used to create Transfer factors and variations for the different control regions (also top regions in case needed)
macros/makeDataHistograms.C : set of functions used to select data and MC events belonging to Z->mm, Z->ee, W->mn, W->en, gamma+jets, SR, ttbar mu, ttbar e.
macros/makeSignalHistograms.C: functions used to apply selections on signal samples -> one function for Higgs invisible vs mH, one function for DM analysis
macros/makeTemplates.C: main code to be run
macros/makeSignalTemplates.C: alternative code just to run on signal samples
To run: cd AnalysisCode/MonoXAnalysis/macros; root -l; .L makeTemplates.C+; makeTemplates(....options ...);
Options:
doCorrectionHistograms : true when you want to prodce root files with Transfer Factors
skipCorrectionHistograms : when true the codes tries to open root files with TFs and clone them in the template file used to create combine workspace
category : it rules the analysis selection 0 means mono=jet inclusive, 1 means mono-jet exclusive, 2 means mono-V .. 3,4,5..etc are used to apply on some sub-structure cuts
lumi : luminosity value in fb-1
outDir : directory where TFs and template file will be created
templateSuffix : suffix to be added to the template file name
observables : different observebales to be considered when created templates {"met","jetPt","njet"} .. names are delcared in macros/histoUtils.h
observables_2D : 2D combinations {"met_ht","met_njet"} ... names are declared in macros/histoUtils2D.h
applyQGLReweight : if true re-weight QGL leading AK8 jet MC to data using pre-existing files in the data directory
doShapeSystematics : if true evaluate MET systematics (JEC, JER, unclustered) and b-tagging SF effects for each MC based process
makeResonantSelection : split ttbar in resonant and non resonant (substructure studies)
typeOfDMSignal : run only on some signal production mechanism .. 0 means both mono-j and mono-V, 1 is mono-j, 2 is mono-V
runHiggsInvisible : run Higgs invisible analysis instead of DM one (when trye the value of typeOfDMSignal doesn't matter)
runOnlySignal : when true apply selections only on signal MC in SR
runOnlyBackground : generate templates for data and bkg processes in SR and CRs
applyPostFitWeights : when true post-fit met binned weights are applied to the event
addTop : to run selections also for the top enriched control samples
ext : additional string
To run signal templates analysis only:
cd AnalysisCode/MonoXAnalysis/macros;
root -l;
.L makeSignalTemplates.C+;
makeSignalTemplates(<options>);
Options:
category : it rules the analysis selection 0 means mono=jet inclusive, 1 means mono-jet exclusive, 2 means mono-V
lumi : luminosity value in fb-1
outDir : output directory
templateSuffix : suffix on root file with templates "outDir+"/templates_signal_"+interactionType+"_"+templateSuffix+".root"
observables : 1D observables list to produce histograms
observables_2D : 2D list to produce histograms
interactionType : string to indicate the type of interaction "Vector","Axial","Scalar","Pseudoscalar"
doShapeSystematics : perform template variations for sys uncertainty
typeOfDMSignal : run only on some signal production mechanism .. 0 means both mono-j and mono-V, 1 is mono-j, 2 is mono-V
runHiggsInvisible : run Higgs invisible analysis instead of DM one (when trye the value of typeOfDMSignal doesn't matter)
ext : additional string
The code to create the workspace assumes that all the templates for CRs and SR (background, data and signal) are located in a common file. In case signal templates have been produced independently from background ones, you can just hadd the templates*.root file.
To run: make sure that the combine tool is properly installed in your CMSSW release
cd AnalysisCode/MonoXAnalysis/macros;
root -l;
gSystem->Load("$CMSSW_BASE/$SCRAM_ARCH/libHiggsAnalysisCombinedLimit.so")
.L createWorksapce.C;
createWorksapce.C(<option>);
Options:
inputName : template file with all the histograms and TFs needed to build the workspace for the Likelihood model
category : same as in the makeTemplates .. used to retrive the right binning for the observable
outputName : name of the output root file with the workspaces
observable : 1D or 2D observable to be considered .. the code does a grep of a substring in the template file (2D are unrolled in 1D histo)
isHiggsInvisible : produce signal templates for higgs invisible analysis (qqH, ggH, wH, zH and ggZH) .. if not monoJ, monoW, monoZ
scaleQCD : to scale the QCD background in the SR
connectWZ : to use the W/Z link for the signal region
connectTop : to link the top-enriched control sample .. not used in the baseline analysis and require to run the makeTemplate with addTop = True
addShapeSystematics : add shape systematics for MC bkg and signal
mergeLeptons : to merge muon and electron control regions (useful as cross check)
isCombination : naming convention used in HIG-16-016
interaction : type of interaction in case of DM analysis
mediatorMass : mass of the higgs boson in case of Higgs invisible, mediator mass for DM analysis
DMMass : DM mass
isCutAndCount : set to true when you want to produce a workspace for a cut and count analysis
normalizeSignal: when doing a cut and count, you can decide to change the signal normalization to a reference value (typically 1 to extract model independent limit on the production cross section)
xAxisSelection: in case of 1D templates, limits to be considered for the cut and count -> should match bin limits of the tempalte.root histograms
yAxisSelection: to be set when performing a 2D analysis, i.e. a 1D un-rolled histograms derived from a 2D (y-axis range of integration for the cut and count)
There is an automatic script to produce all the workspace for all the signal mass points in one shot .. so that each worksapce can be use to run the limit since signal, data and backgroudn templates are there in each root file.
To run the automatic production:
cd AnalysisCode/MonoXAnalysis/;
python scripts/makeWorkspace.py <options>
Options:
--inputDIR: working directory to be used .. typically the same directory in which the template file is located
--outputDIR: output directory where all the workspaces will be copied (typically a directory on afs in the work area)
--templateFile: name of the template file
--category : same meaning as before
--observable: observable to be considered
--addShapeSystematics
--connectWZ
--connectTop
--isHiggsInvisible
--scaleQCD
--mergeLeptons
--isCombination
--batchMode: to use lxbatch as scheduler
--submit : to submit jobs
--jobDIR: directory to put the job executable.. must be accessible from the job itself so typically a directory in afs
--queque: queque name on lxbatch
--interaction: to run workspaces for all the mass combination of a given interaction ("Vector","Scalar","Pseudoscalar","Axial")
--isCutAndCount: to produce a workspace for a cut and count analysis
--normalizeSignal: absolute normalization of the singla for a cut and count analysis
--xAxisSelection : x-axis extreme integration for cut and count
--yAxisSelection : y-axis extreme integration for cut and count
The script works in the following way:
- make a list of all the TKey in the template file
- try to recognize according to the template name the ones belonging to signal samples
- If isHiggsInvisible: make one list for each production mode ggH, qqH, wH, zH and ggZH and submit one job per mass point (only common mass points are considered)
- if not isHiggsInvisible: make a list of each production mode monoJ, monoW and monoZ for a given interaction and submit one job per mass point combination (mMED:mDM), only for common mass points.
Datacards templates used for 2015 results (DM analysis, Higgs Invisible interpretation and combination) are available in the github area at this path: AnalysisCode/MonoXAnalysis/cards. They are organized in different directories .. in particular:
AnalysisCode/MonoXAnalysis/cards/HiggsInvisible : datacards used for Higgs invisible analysis. You cand find two subdirectories, monoJ contains datacards for the monoJet channel, monoV for the monoV(Vhad) one.
AnalysisCode/MonoXAnalysis/cards/monoJet: datacards used for mono-jet channel DM results.
AnalysisCode/MonoXAnalysis/cards/monoJet/onlyMonoJ --> fermion only datacards, in which only mono-jet signal is considered
AnalysisCode/MonoXAnalysis/cards/monoJet/MonoJMonoV --> in the signal region, also contamintion from monoV(Vhad) signals is taken into account
AnalysisCode/MonoXAnalysis/cards/monoJet/CutAndCount --> example cards to be used for a model-independent cross section limit
In each of the aforementioned directory, there are three sub-directories: noShapes means just lnN for b-tag and met systematics, Shapes means use a template morphing (shapeN2) for b-tagging, met sys (jes, jer and unclustered met) and bin-by-bin stat uncertainty for the signal has been added. Finally Top means datacards where two control regions are added (top-mu and top-el) to extract top background from data.
Automatic script can be found in AnalysisCode/MonoXAnalysis/scripts/runCombine.py
Options:
--datacardDIR : directory where the datacard template to be used are located .. they are copied for each job into the job directory in order to substitute the dummy workspace name with the real one considered in the job
--workspaceDIR: directory where all the workspace for each mMED/mDM mass points are located
--jobDIR : in case the script is run in batch mode, jobDIR is the mother directory where job configuration files are located (as well as datacards and workspaces)
--category : as usual, == 0 is monoV+monoJet combination, == 1 is mono-jet exclusive, == 2 is monoV(had)
--interaction : string to indicate "Vector", "Axial", "Scalar","PseudoScalar"
--dmMass : mDM value
--medMass : mediator mass
--batchMode : to allow lxbtach submission mode
--queque : queque on lxbatch to be used when submitting jobs
--submit : to submit jobs
--ntoys : number of toys to be run
--submitOneJobPerToy : one toy == one job .. in particular can be useful when doing bias studies to speed up
--expectSignal : inject a signal strenght in the toy
--toyGeneration : type of toy --> 0 mean frequentist toy (post-fit nuisances from data), 1 means stat only toy a-priori, 2 means a-priori stat+sys toys
--crossedToys : means to use the dataset generated by another toy, probably produced by an alternative model (for un-binned analysis)
--inputGeneratedDataset : generated dataset to be used in the fit/limit/significance computation are stored in a different directory
--Asymptotic : run Asymptotic limit evaluation
--runBlind : to run blind limit (from pre-fit background estimate)
--runExpected : to run just expected limit evaluation
--generateOnly : use the toy generator in combine to produce a pseudo-data genaration
--MaxLikelihoodFit : max likelihood s+b and b-only fit in combine
--MultiDimFit : multi-dimensional fit in combine
--rMin : min r-value bound
--rMax : max r-value bound
--ProfileLikelihood : to be used for significance and p-value computation
--pvalue : compute p-value instead of significance
--makeLikelihoodScan : use MultiDimFit for a Likelihood scan
--outputDIR : output directory where the .root file from combine are located. One root file per job, i.e. one file per mass point. Inside the limit tree the mH branch is fixed to re-flect the mass point, using the following convention: (Vector = 1, Axial = 2, Scalar = 3, PseudoScalar = 4, Higgs Invisible = 5), medMass (4digits), dmMass (4digits). The output dir is created as a subdirectory of the workspaceDIR
--calculateStatOnly: calculate stat only limits or significance (to be used together with --ProfileLikelihood and --Asymptotic) .. this cannot be done with -S 0 with RooParametric hists
--inputSnapShotDIR: directory where snapshot fit file from multiDIM fit are stored