HHbbtautau

Search for two boosted (high transverse momentum) Higgs bosons (H) decaying to two beauty quarks (b) and two tau leptons.

• HHbbtautau
  • Setting up package
    • Creating a virtual environment
    • Installing package
    • Troubleshooting
  • Running coffea processors
    • Setup
    • Running locally
    • Condor jobs
  • Transferring files to FNAL with Rucio

Setting up package

Creating a virtual environment

First, create a virtual environment (micromamba is recommended):

# Clone the repository
git clone --recursive https://github.com/LPC-HH/bbtautau.git
cd bbtautau
# Download the micromamba setup script (change if needed for your machine https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html)
# Install: (the micromamba directory can end up taking O(1-10GB) so make sure the directory you're using allows that quota)
"${SHELL}" <(curl -L micro.mamba.pm/install.sh)
# You may need to restart your shell
micromamba env create -f environment.yaml
micromamba activate hh

Installing package

Remember to install this in your mamba environment.

# Clone the repsitory as above if you haven't already
# Perform an editable installation
pip install -e .
# for committing to the repository
pip install pre-commit
pre-commit install
# Install as well the common HH utilities
cd boostedhh
pip install -e .
cd ..

Troubleshooting

• If your default python in your environment is not Python 3, make sure to use pip3 and python3 commands instead.

• You may also need to upgrade pip to perform the editable installation:

python3 -m pip install -e .

Running coffea processors

Setup

For submitting to condor, all you need is python >= 3.7.

For running locally, follow the same virtual environment setup instructions above and activate the environment.

micromamba activate hh

Clone the repository:

git clone https://github.com/LPC-HH/bbtautau/
pip install -e .

Running locally

For testing, e.g.:

python src/run.py --samples HHbbtt --subsamples GluGlutoHHto2B2Tau_kl-1p00_kt-1p00_c2-0p00 --starti 0 --endi 1 --year 2022 --processor skimmer

Condor jobs

A single sample / subsample:

python src/condor/submit.py --analysis bbtautau --git-branch BRANCH-NAME --site ucsd --save-sites ucsd lpc --processor skimmer --samples HHbbtt --subsamples  GluGlutoHHto2B2Tau_kl-1p00_kt-1p00_c2-0p00 --files-per-job 5 --tag 24Nov7Signal [--submit]

Or from a YAML:

python src/condor/submit.py --yaml src/condor/submit_configs/25Apr5All.yaml --analysis bbtautau --git-branch addmc --site lpc --save-sites ucsd lpc --processor skimmer --tag 25Apr5AddVars --year 2022 [--submit]

Checking jobs

e.g.

python boostedhh/condor/check_jobs.py --analysis bbtautau --tag 25Apr24_v12_private_signal --processor skimmer --check-running --year 2022EE

Postprocessing

Trigger study

Trigger efficiency studies can be performed using the src/bbtautau/postprocessing/TriggerStudy.py script. The main execution logic is within the if __name__ == "__main__" block, where you can configure the years and signal samples to process.

The script will:

• Load the specified signal samples.
• Define trigger sets and tagger configurations.
• Calculate and plot trigger efficiencies for different channels (hh, hm, he).
• Generate N-1 efficiency tables to study the impact of individual triggers.

To run the study, configure the desired years and SIGNALS inside the script and then execute it:

python src/bbtautau/postprocessing/TriggerStudy.py

Output plots and tables will be saved in the plots/TriggerStudy/ directory.

Sensitivity study

python SensitivityStudy.py --actions compute_rocs plot_mass sensitivity --years 2022 2023 --channels hh hm

Arguments

--years (list, default: 2022 2022EE 2023 2023BPix): List of years to include in the analysis. --channels (list, default: hh hm he): List of channels to run (default: all). --test-mode (flag, default: False): Run in test mode (reduced data size). --use-bdt (flag, default: False): Use BDT model for sensitivity study. --modelname (str, default: 28May25_baseline): Name of the BDT model to use. --at-inference (flag, default: False): Compute BDT predictions at inference time. --actions (list, required): Actions to perform. Choose one or more: compute_rocs, plot_mass, sensitivity, time-methods.

Example Commands

Run an optimization analysis for all years and all channels, with the GloParT tautau tagger:

python SensitivityStudy.py --actions sensitivity

Run a full analysis for all years and all channels, using the BDT for the tautau jet:

python SensitivityStudy.py --actions compute_rocs plot_mass sensitivity

Run only on selected years/channels in test mode:

--test-mode will reduce the data loading time significantly. Practical for testing.

python SensitivityStudy.py --actions sensitivity --years 2022 --channels hh --test-mode

Notes:

• by default uses ABCD background estimation method, and FOM = $\sqrt{b+\sigma_b}/s$
• by default uses parallel thread data loading and optimization

Control plots

@Billy - convert into script and add instructions here

BDT

This script provides a command-line interface to train, load, and evaluate a multiclass Boosted Decision Tree (BDT) model on data from one or more years. It includes options for studying rescaling effects, evaluating BDT predictions, and managing data reloading.

Data paths defined in Trainer.__init__ in Trainer.data_path by year and sample type.

python bdt.py [options]

Options:

--years Specify which years of data to store in Trainer object. This establishes which years of data are loaded for training/evaluation. Examples: --years 2022 2022EE 2023BPix or --years all --model Model configuration name (e.g. "test"). Names are keys in /home/users/lumori/bbtautau/src/bbtautau/postprocessing/bdt_config.py configuration dictionaries --save-dir Name to save the trained model and generated plots in "/home/users/lumori/bbtautau/src/bbtautau/postprocessing/classifier/{model_dir}". Defaults to "/home/users/lumori/bbtautau/src/bbtautau/postprocessing/classifier/trained_models/{self.modelname}_{('-'.join(self.years) if not self.years == hh_vars.years else 'all')}" --force-reload Force reloading of data, even if cache/files exist. --samples List of sample names to use for training or evaluation. Defaults to [ggf signals, QCD, ttbar, DY] --train Train a new model (mutually exclusive with --load). --load Load a previously trained model (default if neither is specified). --study-rescaling Script to study the impact of different weight and rescaling rules on BDT performance. --eval-bdt-preds Evaluate BDT predictions on the given data samples and years. Outputs are stored in the data directory as .npy files, and can later be handled through postprocessing.load_bdt_preds.

Example: train a new model ``mymodel''

python bdt.py --train --years all --model mymodel

Models are stored in global CLASSIFIER_PATH defined on top of file.

** Run predictions ** Note: define global variable DATA_PATH in bdt.py for default prediction output directory, or provide a full path in input as .

Templates

These are made using the postprocessing/postprocessing.py script with the --templates option. See postprocessing/bash_scripts/MakeTemplates.sh for an example.

Datacard and fits

CMSSW + Combine Quickstart

Warning: this should be done outside of your conda/mamba environment!

source /cvmfs/cms.cern.ch/cmsset_default.sh
cmsrel CMSSW_14_1_0_pre4
cd CMSSW_14_1_0_pre4/src
cmsenv
scram-venv
cmsenv
git clone -b v10.1.0 https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
git clone -b v3.0.0-pre1 https://github.com/cms-analysis/CombineHarvester.git CombineHarvester
# Important: this scram has to be run from src dir
scramv1 b clean; scramv1 b
pip3 install --upgrade rhalphalib

Then, install this repo as well:

```bash
cd /path/to/your/local/bbtautau/repo
pip3 install -e .

Create datacards

After activating the above CMSSW environment (go inside the CMSSW folder and do cmsenv), you can use the CreateDatacard.py script as so (from your src/bbtautau folder):

python3 postprocessing/CreateDatacard.py --sigs bbtt --templates-dir postprocessing/templates/25Apr25LudoCuts --model-name 25Apr25PassFix

By default, this will create datacards for all three channels summed across years in the cards/model-name directory.

As always, do the following to see a full list of options.

python3 postprocessing/CreateDatacard.py --help

Combine scripts

All combine commands while blinded can be run via the src/bbtautau/combine/run_blinded_bbtt.sh script.

e.g. (always from inside the cards folders), this will combine the cards, create a workspace, do a background-only fit, and calculate expected limits:

run_blinded_bbtt.sh --workspace --bfit --limits

See more comments inside the file.

I also add this to my .bashrc for convenience:

export PATH="$PATH:/home/user/rkansal/bbtautau/src/bbtautau/combine"

Postfit plots

Run the following to run FitDiagnostics and save FitShapes:

run_blinded_bbtt.sh --workspace --dfit

Then see postprocessing/PlotFits.ipynb for plotting. TODO: convert into script!

Transferring files to FNAL with Rucio

Set up Rucio following the Twiki. Then:

rucio add-rule cms:/Tau/Run2022F-22Sep2023-v1/MINIAOD 1 T1_US_FNAL_Disk --activity "User AutoApprove" --lifetime 15552000 --ask-approval