Configurable calorimeter simulation for AI applications

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 partic...

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Bibliographic Details
Published in:Machine learning: science and technology 2023-09, Vol.4 (3), p.35042
Main Authors: Charkin-Gorbulin, Anton, Cranmer, Kyle, Di Bello, Francesco Armando, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Kado, Marumi, Kakati, Nilotpal, Rieck, Patrick, Santi, Lorenzo, Tusoni, Matteo
Format: Article
Language:English
Subjects:
Algorithms
calorimetry
Clustering
Cocoa
editor
inserted
Machine learning
OPEN DATA
Simulation
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Summary: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.
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/acf186