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.
@article{arxiv.2303.02101,
title = {Configurable calorimeter simulation for AI applications},
author = {Francesco Armando Di Bello and Anton Charkin-Gorbulin and Kyle Cranmer and Etienne Dreyer and Sanmay Ganguly and Eilam Gross and Lukas Heinrich and Lorenzo Santi and Marumi Kado and Nilotpal Kakati and Patrick Rieck and Matteo Tusoni},
journal= {arXiv preprint arXiv:2303.02101},
year = {2023}
}