Events

Event implementations for different physics analyses and data formats.

Overview

The events module contains concrete implementations of the event template for various physics analyses:

  • bsm_4tops: Four-top quark BSM analysis events

  • exp_mc20: Experimental MC20 dataset events

  • gnn: Graph neural network inference events

  • ssml_mc20: Semi-supervised machine learning MC20 events

Each event implementation defines:

  • Particle collections (Jets, Electrons, Muons, etc.)

  • Event-level variables (MET, weights, etc.)

  • Truth-level information for MC

  • Build and compilation logic

BSM 4-Tops Event

Event class for four-top quark BSM physics analysis. Contains:

  • Top quark collections

  • Children particles from top decays

  • Truth jets

  • Detector-level jets, electrons, muons

  • Event metadata (event number, pileup, MET)

Experimental MC20 Event

General-purpose event class for MC20 simulation samples. Provides:

  • Standard detector objects

  • Truth-level particles

  • Event weights and identifiers

GNN Event

Specialized event class optimized for graph neural network applications:

  • Simplified particle collections

  • Graph-friendly data structures

  • Inference-optimized layout

SSML MC20 Event

Event class designed for semi-supervised machine learning on MC20:

  • Additional particle types (leptons, jets)

  • Extended truth information

  • MET and kinematics

Event Relationships

All event classes inherit from event_template and implement:

  1. clone(): Create a copy of the event instance

  2. build(): Populate event from ROOT data

  3. CompileEvent(): Finalize event after building

Event classes manage particle collections through:

  • Public vectors of particle_template* for user access

  • Private maps for efficient particle lookup during building

  • Automatic vectorization and sorting