Configurable calorimeter simulation for AI applications

TL;DR

The paper introduces COCOA, a configurable, open-source calorimeter simulation tool based on Geant4 and Pythia, designed to aid AI-driven high energy physics analysis with realistic particle shower modeling.

Contribution

It presents a flexible, user-configurable simulation platform integrated with event processing and visualization tools for AI applications in high energy physics.

Findings

Supports realistic particle shower simulations for AI

Enables customizable geometry and materials

Includes event processing and visualization features

Abstract

A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.