CalorimeTree

Analysis framework for isolated photons, neutral pions, jets, and correlation observables from ALICE tree-based inputs (AliPhysics/O2Physics style).

Quick start

1. Edit one of the run configs (for example RunConfig.yaml) so dataset paths match your environment.
2. Run the main driver:
   • python3 start.py --config RunConfig.yaml
3. Enable workflow steps in the config (doMakeHistos, doPurity, doPlotting, ...) as needed.

Available flags at a glance

Flag	Macro/script run by start.py	Minimal description
doMakeHistos	Analysis/makeHistosFromTree.C	Produces HistosFromTree*.root from input tree files.
doPlotting	Analysis/plotHistosFromTree.C	Creates standard QA/physics plots from histogram outputs.
doAnalysisExclGammaJet	Analysis/analyseExclGammaJet.C	Runs exclusive gamma-jet analysis from produced histograms.
doPlottingExclGammaJet	Analysis/plotExclGammaJet.C	Plots and summarizes exclusive gamma-jet analysis results.
doCombineExclGammaJet	Analysis/combineExclGammaJet.C	Merges exclusive gamma-jet outputs across datasets/cuts.
doPurity	Analysis/calculatePurity.C	Computes purity-related quantities and derived histograms.
doPlottingPurity	Analysis/plotPurity.C	Produces plots for purity study outputs.
doIsoGamma	(no standalone macro; used inside makeHistosFromTree.C)	Enables isolated-photon observables during histogram production.
doJets	(no standalone macro; used inside makeHistosFromTree.C)	Enables jet-related observables during histogram production.
doGGPi0	(no standalone macro; used inside makeHistosFromTree.C)	Enables gamma-gamma neutral-pion channel filling.
domPi0	(no standalone macro; used inside makeHistosFromTree.C)	Enables invariant-mass neutral-pion channel filling.
doSubstructure	(no standalone macro; used inside makeHistosFromTree.C)	Enables jet-substructure observable filling.

Main entry points

• start.py: orchestrates ROOT macro execution and optional SLURM submission.
• Analysis/: core C++ analysis and plotting macros plus shared headers.
• Cuts.yaml: central cut definitions used by analysis macros.
• RunConfig*.yaml: predefined campaign-specific run configurations.
• ML/: preparation, training, and evaluation scripts/macros for ML-based studies.
• tools/: conversion, download, job recovery, and full-chain helper scripts.
• DummyDataSet/: minimal example input structure for local testing.

Typical workflow

1. Prepare or convert input trees (helper scripts in tools/).
2. Configure datasets/train configs in a RunConfig*.yaml file.
3. Run start.py to produce histograms and optional derived outputs.
4. Post-process with plotting/purity macros in Analysis/ or ML scripts in ML/.