Running the analysis¶

The pipeline — anaTuples, observables, histograms, plots — is the standard FLAF chain; follow the FLAF full-workflow walkthrough for the commands. This page collects what is specific to HH→bb̄WW.

ERA=Run3_2022
VER=dev

law run FLAF.Analysis.tasks.HistPlotTask --period $ERA --version $VER --workflow local --test 1000

The b-tag-shape cache runs first¶

HH→bb̄WW computes b-tag shape weights in a dedicated caching stage, AnalysisCacheTask (aggregated by AnalysisCacheAggregationTask), which LAW runs automatically before histogramming — see FLAF → Task reference.

Budget time for the cache on a cold start

On a cold cache AnalysisCacheTask is slow (roughly an hour per branch), even for simple variables. When iterating, reuse an existing cache instead of recomputing it, using a per-task version override, e.g. --AnalysisCacheTask-version <existing> --AnalysisCacheAggregationTask-version <existing>.

Mass reconstruction: DeepHME¶

The HH mass is reconstructed with DeepHME (the bb̄WW counterpart to SVfit in bb̄ττ). It is part of the observable computation and requires no special command — it runs as part of the standard producer chain.

Categories¶

HH→bb̄WW is analysed in resolved and boosted categories (low- and high-p_T HH topologies). Category and channel selection is driven by config/global.yaml; narrow or extend it there or via your user_custom.yaml.

Choosing which variables to histogram¶

As for any FLAF analysis, the variable set is controlled by the variables: list in user_custom.yaml (or --variables). A short list keeps test runs fast:

variables:
  - lep1_pt
  - ggF_DNN_HH

Statistical interpretation¶

Continue to Statistical inference for datacards, limits and diagnostics.