Repository for high mass diphoton analysis.

Checking out the code

This package depends on the flashgg.

A quick set of commands to check out the code is below. They assume that you have set your user github name in your git config and that you have forked the flashgg and this repository.

# change these to the values that you prefer
## # Phys14 settings
## PROJECT_AREA=EXO_7_4_0_pre9 
## CMSSW_VERSION=CMSSW_7_4_0_pre9 
## FLASHGG_TAG=diphtons_phys14

# Spring15 settings
PROJECT_AREA=EXO_7_4_6_patch2
CMSSW_VERSION=CMSSW_7_4_6_patch2 
FLASHGG_TAG=Spring15BetaV2_MetaV1 # set empty if you want the master (safe as long as you are not producing MicroAOD)

# read github name from git config
MY_GITHUB_NAME=$(git config --get user.github)

scram project -n ${PROJECT_AREA} CMSSW ${CMSSW_VERSION}
cd ${PROJECT_AREA}/src

cmsenv 

cd ${CMSSW_BASE}/src
git cms-init

# clone flashgg 
cd ${CMSSW_BASE}/src
git clone https://github.com/cms-analysis/flashgg.git

## make sure we use a consistent flashgg tag
cd flashgg
#git remote add musella [email protected]:musella/flashgg.git
#git fetch musella
#[[ -n ${FLASHGG_TAG} ]] && git co -b topic_${FLASHGG_TAG} ${FLASHGG_TAG} 
git checkout -b Spring15BetaV2 Spring15BetaV2

cd ${CMSSW_BASE}/src
bash flashgg/setup.sh 2>&1 | tee flashgg/setup.log

# add your own fork as a remote. Skip if you dont have one
cd flashgg 
git remote add ${MY_GITHUB_NAME} [email protected]:${MY_GITHUB_NAME}/flashgg.git
git fetch ${MY_GITHUB_NAME}

# clone this repository
cd ${CMSSW_BASE}/src
git clone https://github.com/musella/diphotons.git

# add your own fork as a remote. Skip if you dont have one
cd diphotons
git remote add ${MY_GITHUB_NAME} [email protected]:${MY_GITHUB_NAME}/diphotons.git
git fetch ${MY_GITHUB_NAME}

# now compile everything
cd ${CMSSW_BASE}/src
scram b -j 16

Producing microAODs

The so-called microAOD format is a subset of the MINIAOD produced by the flashgg framework, adding specific photon-related informations.

Only high level objects such as photons, leptons, jets and MET are stpored in microAD by default. If additional informations are needed, they need to be added explicitely to the output.

The package flashgg/MetaData contains the scripts related to microAOD production. To start with, go to flashgg/MetaData/work.

cmsenv
cd ${CMSSW_BASE}/src/flashgg/MetaData/work 

The folder contains the script prepareCrabJobs.py, which sets up the crab3 tasks needed to generate microAODs.

First, you need to set up the crab3 and grid environment.

source /cvmfs/cms.cern.ch/crab3/crab.sh
voms-proxy-init --voms cms --valid 168:00

Then, prepare a json file with the list of datasets to be processed, as in the example below.

{
    "data" : ["/DoubleElectron/CMSSW_7_0_6_patch1-GR_70_V2_AN1_RelVal_zEl2012D-v1/MINIAOD"
              ],
    "sig"  : ["/GluGluToHToGG_M-125_13TeV-powheg-pythia6/Spring14miniaod-PU20bx25_POSTLS170_V5-v2/MINIAODSIM",
              "/VBF_HToGG_M-125_13TeV-powheg-pythia6/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM",
              "/WH_ZH_HToGG_M-125_13TeV_pythia6/Spring14miniaod-PU20bx25_POSTLS170_V5-v2/MINIAODSIM",
              "/TTbarH_HToGG_M-125_13TeV_pythia6/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM",
              "/TTbarH_HToGG_M-125_13TeV_amcatnlo-pythia8-tauola/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM",
              "/GluGluToHHTo2B2G_M-125_13TeV-madgraph-pythia6/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM"
              ],
    "bkg"  : ["/GJet_Pt20to40_doubleEMEnriched_TuneZ2star_13TeV-pythia6/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM",
              "/GJet_Pt40_doubleEMEnriched_TuneZ2star_13TeV-pythia6/Spring14miniaod-PU20bx25_POSTLS170_V5-v2/MINIAODSIM",
              "/DYJetsToLL_M-50_13TeV-madgraph-pythia8-tauola_v2/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM"
              ]
}

To generate the crab3 configuration run (see ./prepareCrabJobs.py --help for the full list of options).

./prepareCrabJobs.py -p <parameterSet> \
		     -C <microAODCampaginName> \
		     -s <jsonFileWithDatasetList> \
		     --mkPilot

This will create a folder containing all the crab3 configuration files, the CMSSW parameterSet as well as a file called config.json containing the dump of all the script options. The latter can be used to reproduce the same configuration (using the --load option), eg when adding more dataset to the same campaign.

./prepareCrabJobs.py --load <previous_config.json> \
		     -s <jsonFileWithAdditionalDatasets>

The --mkPilot option will create an additional crab3 configuration that can be used to run on a single file, before submitting the whole list of tasks.

You can now lanch the microAOD production.

cd <microAODCampaginName>
echo crabConfig_*.py | xargs -n 1 crab sub
## or using GNU parallel 
## parallel 'crab sub {}' ::: crabConfig_*.py

Exisiting configurations

cmsenv

source /cvmfs/cms.cern.ch/crab3/crab.sh
voms-proxy-init --voms cms --valid 168:00

## CAMPAIGN=AN_Phys14_samples
CAMPAIGN=EXOSpring15_v1

cd ${CMSSW_BASE}/src/flashgg/MetaData/work

cp -p  ${CMSSW_BASE}/src/diphotons/MetaData/work/campaigns/${CAMPAIGN}.json campaigns/My${CAMPAIGN}.json
ln -sf  ${CMSSW_BASE}/src/diphotons/MetaData/work/analysis_microAOD.py .

# allow running on invalid datasets
cat crabConfig_TEMPLATE.py > mycrabConfig_TEMPLATE.py
cat >> mycrabConfig_TEMPLATE.py << EOF

config.Data.allowNonValidInputDataset=True
EOF

# edit list of samples to be actually submitted (and check the crab template)
emacs -nw campaigns/My${CAMPAIGN}.json mycrabConfig_TEMPLATE.py

./prepareCrabJobs.py -C ${CAMPAIGN} -s campaigns/My${CAMPAIGN}.json -p analysis_microAOD.py  --mkPilot -t mycrabConfig_TEMPLATE.py

# submit pilot jobs
cd ${CAMPAIGN}
echo pilot* | xargs -n 1 crab sub

Running on microAODs

Set up file catalog

See instructions in flashgg/MetaData/README.md

File catalog for ExoPhys14ANv1 is already in git (ie you can skip this part). To see the list of datasets run:

fggManageSamples.py -m diphotons -C ExoPhys14ANv1 list

Jobs configuration

Full-framework and FWLite configurations are stored in Analysis/config. Example of how to run them:

cd Analysis/config
cp -p jobs_gamjets_gamgam.json myjobs.json
emacs -nw photonIdAnalyzer.py myjobs.json

# test with 100 events
fggRunJobs.py --load myjobs.json -d myjobs ./photonIdAnalyzer.py maxEvent=100

# submit all jobs. add -q <queue_name> to run in batch
fggRunJobs.py --load myjobs.json -d myjobs -H -D -P -n 5 ./photonIdAnalyzer.py maxEvent=-1

Configuration files

• Analysis/config/high_mass_analysis.py: full analysis with signal region and sidebands.
• Analysis/config/regressionTreeAnalyzer.py: regression training tree producer.
• Analysis/config/photonIdAnalyzer.py, Analysis/config/photonIdDumper.py: photon ID training tree producer.

JSON files containing list of processes.

• Analysis/config/jobs_gamgam.json : only GGJets sherpa
• Analysis/config/jobs_gamjets.json : only GJets madgraph
• Analysis/config/jobs_gamjets_gamgam.json : all processes

Macros

Macros are stored in Analysis/macros.

They tipically come with associated JSON file for configuration. Python macros are run as: ./macroName.py --load macroConfig.json [additional options, typilcally --input-dir <input dir> -O <output dir>]

The --help option gives a list of supported options, while the --dumpConfig dumps all options in JSON format (useful to record a particular configuration).

• Analysis/macros/idEvolution.py: photon ID studies. Runs on the output of Analysis/config/photonIdAnalyer.py or photonIdDumper.py.
• Analysis/macros/basic_plots.py: basic di-photon plots. Runs on the output of Analysis/config/high_mass_analysis.py.
• Analysis/macros/eff_plots.py: efficiency for di-photon selection. Runs on the output of Analysis/config/high_mass_analysis.py.
• Analysis/macros/templates_maker.py: deals with templates creation.