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.
Configurable calorimeter simulation for AI applications / Charkin-Gorbulin, A.; Cranmer, K.; Di Bello, F. A.; Dreyer, E.; Ganguly, S.; Gross, E.; Heinrich, L.; Kado, M.; Kakati, N.; Rieck, P.; Santi, L.; Tusoni, M.. - In: MACHINE LEARNING: SCIENCE AND TECHNOLOGY. - ISSN 2632-2153. - 4:3(2023), pp. 1-11. [10.1088/2632-2153/acf186]
Configurable calorimeter simulation for AI applications
Di Bello F. A.;Kado M.;Santi L.;Tusoni M.
2023
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.| File | Dimensione | Formato | |
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CharkinGorbulin_Configurable-calorimeter-simulation_2023.pdf
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