2023/03/03 by F. A. Di Bello, Anton Charkin-Gorbulin, Di Bello, Francesco Armando +21 · 1 citation
Decision Sciences · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Instrumentation and Detectors (physics.ins-det) #Machine Learning (cs.LG) #Particle Detector Development and Performance #Radiation Effects in Electronics #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2303.02101
openalex publication_date 2023/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.