Configurable calorimeter simulation for AI applications
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 3 Mar 2023.
Builds on
4 of 19 references.
References · 4
- 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
- 2021A Living Review of Machine Learning for Particle Physicscited by 77
- 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
- 2020Towards a Computer Vision Particle Flowcited by 15
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.