Boosted HHbbtautau skims with NanoHRT-tools

Set up CMSSW and offcial NanoAOD-tools

cmsrel CMSSW_15_0_13
cd CMSSW_15_0_13/src
cmsenv

git cms-addpkg PhysicsTools/NanoAODTools

Get customized NanoAOD tools for HeavyResTagging (NanoHRT-tools), and pick the right branch for HH analysis

git clone https://github.com/slaurila/NanoHRT-tools.git PhysicsTools/NanoHRTTools -b dev/hhbbtautau

Compile

scram b -j8

Skim

cd PhysicsTools/NanoHRTTools/run

Use this command to submit the skimming jobs to condor:

python3 runPostProcessing.py -i [input-data-or-mc-dir-with-nanoaods] --cut [cutstring] -o [output-dir-on-eos] --json [lumi-json] -n [number-of-jobs] --bi [keep-and-drop-input-branch-list] --bo [keep-and-drop-output-branch-list] -d [yaml-file-with-input-sample-list] -j [jobs-directory-name]

For example, to skim 2018 data:

python3 runPostProcessing.py -i /eos/cms/store/cmst3/group/hh/NanoAOD/20250725_v15/2018/data/ --cut '(HLT_AK8PFHT800_TrimMass50 || HLT_AK8PFJet400_TrimMass30 || HLT_AK8PFJet500 || HLT_PFJet500 || HLT_PFHT1050 || HLT_PFHT500_PFMET100_PFMHT100_IDTight || HLT_PFHT700_PFMET85_PFMHT85_IDTight || HLT_PFHT800_PFMET75_PFMHT75_IDTight || HLT_IsoMu24 || HLT_Mu50 || HLT_Ele32_WPTight_Gsf || HLT_Ele115_CaloIdVT_GsfTrkIdT || HLT_Photon200 || DoubleMediumChargedIsoPFTauHPS35_Trk1_eta2p1_Reg || HLT_MediumChargedIsoPFTau180HighPtRelaxedIso_Trk50_eta2p1) && nFatJet >=2 && Sum$(FatJet_particleNet_XbbVsQCD>0.1 && FatJet_mass*FatJet_ParticleNet_raw_masscorr>50)>0 && Sum$((FatJet_ParticleNet_raw_probHtt>0.1 || FatJet_ParticleNet_raw_probHtm>0.1 || FatJet_ParticleNet_raw_probHte>0.1) && FatJet_mass*FatJet_ParticleNet_raw_masscorr>30)>0 && Sum$(FatJet_pt>250)>0' -o /eos/cms/store/cmst3/group/hh/NanoAODskimmed/20250908_NanoAODv15_2018_skim_v2/data --json $CMSSW_BASE/src/PhysicsTools/NanoHRTTools/data/JSON/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt -n 40 --bi keep_and_drop_boostedtaus_hh.txt --bo keep_and_drop_boostedtaus_hh.txt -d custom_samples_hh/data_2018.yaml --condor-extras '+AccountingGroup = "group_u_CMST3.all"' -j jobs_20251029_2018_skim_v2_data

To skim 2018 MC:

python3 runPostProcessing.py -i /eos/cms/store/cmst3/group/hh/NanoAOD/20250725_v15/2018/mc/ --cut '(HLT_AK8PFHT800_TrimMass50 || HLT_AK8PFJet400_TrimMass30 || HLT_AK8PFJet500 || HLT_PFJet500 || HLT_PFHT1050 || HLT_PFHT500_PFMET100_PFMHT100_IDTight || HLT_PFHT700_PFMET85_PFMHT85_IDTight || HLT_PFHT800_PFMET75_PFMHT75_IDTight || HLT_IsoMu24 || HLT_Mu50 || HLT_Ele32_WPTight_Gsf || HLT_Ele115_CaloIdVT_GsfTrkIdT  || HLT_Photon200 || HLT_DoubleMediumChargedIsoPFTauHPS35_Trk1_eta2p1_Reg || HLT_MediumChargedIsoPFTau180HighPtRelaxedIso_Trk50_eta2p1) && nFatJet >=2 && Sum$(FatJet_particleNet_XbbVsQCD>0.1 && FatJet_mass*FatJet_ParticleNet_raw_masscorr>50)>0 && Sum$((FatJet_ParticleNet_raw_probHtt>0.1 || FatJet_ParticleNet_raw_probHtm>0.1 || FatJet_ParticleNet_raw_probHte>0.1) && FatJet_mass*FatJet_ParticleNet_raw_masscorr>30)>0 && Sum$(FatJet_pt>250)>0' -o /eos/cms/store/cmst3/group/hh/NanoAODskimmed/20251029_NanoAODv15_2018_skim_v2/mc -n 40 --bi keep_and_drop_boostedtaus_hh.txt --bo keep_and_drop_boostedtaus_hh.txt -d custom_samples_hh/mc_2018.yaml -j jobs_20251029_2018_skim_v2_mc

For 2016, use this trigger list instead:

(HLT_AK8PFHT700_TrimR0p1PT0p03Mass50 || HLT_PFHT900 || HLT_AK8PFJet360_TrimMass30 || HLT_AK8PFJet450 || HLT_DoubleMediumIsoPFTau35_Trk1_eta2p1_Reg || HLT_LooseIsoPFTau50_Trk30_eta2p1_MET90 || HLT_VLooseIsoPFTau140_Trk50_eta2p1 ||  HLT_IsoMu24 || HLT_Mu50 or HLT_Ele32_eta2p1_WPTight_Gs || HLT_Ele115_CaloIdVT_GsfTrkIdT || HLT_Photon175)

For 2017, use this trigger list instead:

HLT_AK8PFJet500 or HLT_PFJet500 || HLT_PFHT1050 || HLT_PFHT500_PFMET100_PFMHT100_IDTight || HLT_PFHT700_PFMET95_PFMHT95_IDTight || HLT_PFHT800_PFMET85_PFMHT85_IDTight || HLT_MonoCentralPFJet80_PFMETNoMu120_PFMHTNoMu120_IDTight || HLT_PFMETNoMu120_PFMHTNoMu120_IDTight || HLT_PFMET120_PFMHT120_IDTight || HLT_MediumChargedIsoPFTau180HighPtRelaxedIso_Trk50_eta2p1 || HLT_MediumChargedIsoPFTau50_Trk30_eta2p1_1pr_MET100 || HLT_IsoMu27 || HLT_Mu50 || HLT_Ele35_WPTight_Gsf || HLT_Photon200)

Remember to also update the input and output paths and the golden json for each year.

One all samples are processed, you can merge them by running:

python3 runMerge.py -o [output-directory] -d [sample-list-yaml] -j [job-directory]

For example, for 2018 data:

python3 runMerge.py -o /eos/cms/store/cmst3/group/hh/NanoAODskimmed/20250908_NanoAODv15_2018_skim_v2/data -d custom_samples_hh/data_2018.yaml -j jobs_20251029_2018_skim_v2_data

Generic isntructions

Test

Instructions to run the nanoAOD postprocessor can be found at nanoAOD-tools.

Production

cd PhysicsTools/NanoHRTTools/run

Make trees for MC performance study:

python runPostProcessing.py [-i /path/of/input] -o /path/to/output -d datasets.yaml --friend 
-I PhysicsTools.NanoHRTTools.producers.hrtMCTreeProducer hrtMCTree -n 1

To merge the trees, run the same command but add --post -w '' (i.e., set -w to an empty string ('') -- we do not add the cross sections, but simply reweight signals to match the QCD spectrum afterwards).

Make trees for heavy flavour tagging (bb/cc) or top/W data/MC comparison and scale factor measurement:

python runHeavyFlavTrees.py -i /eos/uscms/store/user/lpcjme/noreplica/NanoHRT/path/to/input -o /path/to/output 
(--sample-dir custom_samples) --jet-type [ak8,ak15] --channel [photon|qcd|muon|inclusive] --year [2016|2017|2018] -n 10 
(--batch) (--run-data) (--run-syst)
(--run-tagger) (--run-mass-regression) (--sfbdt 0.5)
(--condor-extras '+AccountingGroup = "group_u_CMST3.all"')

Command line options:

• the preselection for each channel is coded in runHRTTrees.py
• add --run-data to make data trees
• add --run-syst to make the systematic trees
• can run data & MC for multiple years together w/ e.g., --year 2016,2017,2018. The --run-data option will be ignored in this case. Add also --run-syst to make the systematic trees.
• use --sample-dir to specify the directory containing the sample lists. Currently we maintain two sets of sample lists: the default one is under samples which is used for running over official NanoAOD datasets remotely, and the other one is custom_samples which is used for running over privately produced NanoAOD datasets locally. To run over the private produced samples, ones needs to add --sample-dir custom_samples to the command line.
• the --batch option will submit jobs to condor automatically without confirmation
• remove -i to run over remote files (e.g., official NanoAOD, or private NanoAOD published on DAS); consider adding --prefetch to copy files first before running
• [NEW] add --run-tagger (--run-mass-regression) to run new ParticleNet tagger (mass regression) on-the-fly. Check HeavyFlavBaseProducer.py for the model configuration.
• [NEW] use --sfbdt to change the sfBDT cut value. This affects only QCD and photon samples. By default, sfBDT > 0.5 is applied to QCD and photon samples.
• [NEW] use --condor-extras to pass extra options to condor job description file.

More options of runPostProcessing.py or runHRTTrees.py (a wrapper of runPostProcessing.py) can be found with python runPostProcessing.py -h or python runHRTTrees.py -h, e.g.,

• To resubmit failed jobs, run the same command but add --resubmit.

• To add cross section weights and merge output trees according to the config file, run the same command but add --post. The cross section file to use can be set with the -w option.

Truth-matching criteria

For maximal flexibility, a number of truth-matching varibles are defined in HeavyFlavBaseProducer for hadronically decaying top quarks and W, Z, Higgs bosons. For W/Z/H we define:

• fj_idx_dr_X: deltaR of the fatjet to the nearest hadronically decaying X particle. If found, this top quark X is then used to define all the following variables. Default to 99 if no hadronically decaying X in the event.
• fj_idx_dr_X_daus: max deltaR between the fatjet and the two quarks from X decay.
• fj_idx_X_pt: pt of X
• fj_idx_X_decay: max abs(pdgId) of the two quarks from X decay. For H/Z, this means 5: bb, 4: cc, <4: qq. For W, this means 4: cx, <4: qq. Default to 0 if no hadronically decaying X in the event.

Top quark is treated a bit differently:

• fj_idx_dr_T: deltaR of the fatjet to the nearest hadronically decaying top quark. If found, this top quark T is then used to define all the following variables. Default to 99 if no hadronically decaying top in the event.
• fj_idx_dr_T_b: deltaR between the fatjet and the b quark from the hadronic T decay.
• fj_idx_dr_T_Wq_(max|min): max|min deltaR between the fatjet and the two quarks from the W decay.
• fj_idx_T_Wq_(max|min)_pdgId: pdgId (w/o taking the absolute value) of the corresponding two quarks from W decay.
• fj_idx_T_pt: pt of T

Truth-matching criteria for top/W tagging scale factors

• top-matched: all three quarks contained in the fatjet
  • fj_1_dr_T_b<jetR && fj_1_dr_T_Wq_max<jetR
• W-matched: only the two W quarks contained, the b quark is outside the jet cone (if the W is from top quark decay)
  • ((fj_1_T_Wq_max_pdgId==0 && fj_1_dr_W_daus<jetR) || (fj_1_T_Wq_max_pdgId!=0 && fj_1_dr_T_b>=jetR && fj_1_dr_T_Wq_max<jetR))
  • [Note] the first part is mainly intended for tW events where the top quark decays leptonically, and the W boson decays hadronically. This can be a sizeable contribution to the W-matched events and needs to be taken into account properly. The trick here makes use of the fact that fj_1_T_Wq_max_pdgId is non-zero only if there is a hadronic top in the event.
• unmatched: defined as (NOT top-matched) and (NOT W-matched), i.e.,
  • !(fj_1_dr_T_b<jetR && fj_1_dr_T_Wq_max<jetR) && !((fj_1_T_Wq_max_pdgId==0 && fj_1_dr_W_daus<jetR) || (fj_1_T_Wq_max_pdgId!=0 && fj_1_dr_T_b>=jetR && fj_1_dr_T_Wq_max<jetR))

[Extra] For selecting specifically W->cx decays from the W-matched jets:

• W(cx)-matched:
  • ((fj_1_T_Wq_max_pdgId==0 && fj_1_dr_W_daus<jetR && fj_1_W_decay==4) || (fj_1_T_Wq_max_pdgId!=0 && fj_1_dr_T_b>=jetR && fj_1_dr_T_Wq_max<jetR && (abs(fj_1_T_Wq_max_pdgId)==4 || abs(fj_1_T_Wq_min_pdgId)==4)))

Checklist when updating to new data-taking years / production campaigns

• triggers
• lumi values
• golden JSON
• PU rewgt
• lepton ID/ISO
• b-tag WP
• JEC/JER
• MET filters
• MET recipes (if any)
• samples (check also those in PRODUCTION status)