- Analysis Plan
- TODOs
- Plan
- In progress:
- ~Completed:
- Pileup
- JES/R http://cds.cern.ch/record/2792322/files/DP2021_033.pdf
- Need to update to latest
- JMS/R http://cds.cern.ch/record/2256875/files/JME-16-003-pas.pdf
- Need UL mSD and regressed mass corrections
- Top pt
- Theory
- pdf uncertainties
- scale variation
- parton shower weights
- W k factor
- Pileup
- ParticleNet Xbb
- Split up uncertainties
- JES/R
- JMS/R
- Trigger SFs
- Stat. Unc.
- Correlated Syst.
- Top pt
- Theory
- pdf uncertainties
- parton shower weights
- scale variation
- W k factor
- MC Stats
- Lumi
- Pileup
- Trigger SFs
- Stat.
- Correlated Syst.
- ParticleNet Xbb
- Separate uncertainties
- JES/R
- JMS/R
- Top pT
- Theory
- BR
- pdf uncertainties
- rate
- shape?
- QCD scale
- alpha_s (for single Higgs)
- parton shower weights
- scale variation?
- W k factor??
- New e, mu, b-tag jets selections from VHbb
- Re-run with VV regressed mass for Dijet variables
- FatJet ID
- Look at ID scale factors
- New LP Method
- Update LP method
- BDT ROC
- Try multi-class BDT
- Try equalizing background weights
- Optimize hyperparams
- Trim features
- Check sculpting
- Scan Txbb, BDT WPs
- Run over all kL and k_2V
- Re-run VBF with gen selection
- HH inference
- Theory uncertainties
- GoF
- F-test
- Impacts
- Control plots
- Preliminary signal region
- Validation region
- 2D fit, xUL
- GoF
- F-test
- Run over all signals
- TRSM, NMSSM exclusions
- Scan Txbb, THWW working points
- Update LP
- JECs for AK4 Jets
- DeepJet Btag
- B-tag scale factors
- muon ID SFs
- Save regressed mass
- Jet ID SFs (not required) https://twiki.cern.ch/twiki/bin/view/CMS/JetID13TeVUL
- PFNano on SingleMuon to get regressed mass
- Test veto on 2 AK8 W-tagged jets
May not be necessary given low yield
- Tagger efficiency
- Recoil
- JMS
- Regressed mass
- BDT
- Check VBF?
Raghav:
- Trigger efficiencies
- Add to skimmer:
- num e, mu
- e, mu 4 vectors
- ak4 b jets (medium btag) - outside bb jet
- check control plots on num_x
- Check vetoes on these
Cristina
- Systematics
Raghav:
- 2017 fits
- PR: Systematics into datacard
Cristina:
- update xsecs
- Goodness-of-fit, f-test, impacts
Raghav:
- Full run 2, all samples
- Wrap up nonresonant for now with full run 2 limits and GoF
- Develop resonant analysis strategy
- Plots, selections for subset of signals
- Get 2D fit working
- Get 2D fit working
- Upper limits for subset of signals
- Upper limits for all signals!
- Complete v1 of AN
- Higher mass ParT training samples
- Complete v1 of AN
- B2G Workshop talk
- F-test
- Start WP Scan
- Update semi-leptonic ttbar processor
- Twiki
- Respond to comments
- Samples
- Nanogen
- Skimming VBF
- Running over kL, k2V
- using a coarse-grained grid search on pT, msd, and tagger scores
- 2017 lumi + data only
- measured for two AK8 fat jets, two AK15 fat jets, and a hybrid (AK8 for bb candidate, AK15 for VV candidate)
- background estimation from data sidebands
- with AK8 and AK15 mass-decorrelated ParticleNet Hbb taggers + NOT mass-decorrelated AK8 ParticleNet H4q tagger
-
Measured for AK8, AK15, and hybrid jets, single-jet 2D (mass, pT binned) efficiencies (applied assuming prob. of each fat jet passing trigger is independent) and 3D (jet 1 mass, jet 1 pt, jet 2 pt binned) efficiencies (processors)
-
Decided on AK8 only - ~same sensitivity, significantly easier practically
-
Measure for all years
-
Update selection
-
Check if binning in VV tagger is necessary (probably not since only btag is in the trigger)
-
Investigate high unc.
https://github.com/rkansal47/HHbbVV/blob/main/processors/bbVVSkimmer.py
- Currently includes:
- Signal gen-matching cuts
- Pre-selection kinematic cuts
- Inference via triton server running on SDSC
- Lund plane scale factors for skimmer
- Saving flat skimmed data to parquet files, and metadata (total events, cutflow) to pickles
- FatJet selections
- Modify JECs code to save only variations of pT
- Add JMS/R
- Regressed mass cut
- Add e, mu, b-tag jets
- Add tagger vars
- Dijet variables
https://github.com/rkansal47/sonic-models https://gitlab.nrp-nautilus.io/raghsthebest/triton-server
Server for running inference with our new HWW tagger on samples.
https://github.com/rkansal47/HHbbVV/blob/main/src/HHbbVV/scripts/pickle_scripts/TrainBDT.py
HWW tagger + mass regression development (Zichun's tagger repo and Ish's regression repo)
- JetHT
- QCD
- TTbar
- ST
- W, Z+jets
- Diboson
- HHbbWW (all kL)
- Need xsecs for:
https://docs.google.com/spreadsheets/d/1XQQsN4rl3xGDa35W516TwKyadccpfoT7M1mFGxZ4UjQ/edit#gid=1223976475
- VBF HHbbWW (kL = 1, k2V = 1)
- HH4b (kL = 1) (Pre-UL)
- HWW (ggF, VH, VBF, ttH)
- Hbb (ggF, VBF, VH, ttH)
- Implemented and validated for top jets in control region
- Implemented and measured for nonresonant signal
- Check control plots for all years
- Re-train BDT for all years
- Templates, systematics for all years
- Update datacard with all years