https://doi.org/10.1140/epjp/s13360-024-05397-4
Regular Article
CaloShowerGAN, a generative adversarial network model for fast calorimeter shower simulation
1
Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma Tor Vergata, 00133, Rome, Italy
2
Department of Physics, University of Wisconsin, 53706, Madison, WI, USA
3
Microtechnology and Nanoscience, Chalmers University of Technology, SE-412 96, Goteborg, Sweden
Received:
30
March
2024
Accepted:
25
June
2024
Published online:
8
July
2024
In particle physics, the demand for rapid and precise simulations is rising. The shift from traditional methods to machine learning-based approaches has led to significant advancements in simulating complex detector responses. CaloShowerGAN is a new approach for fast calorimeter simulation based on generative adversarial network (GAN). We use Dataset 1 of the Fast Calorimeter Simulation Challenge 2022 to demonstrate the efficacy of the model to simulate calorimeter showers produced by photons and pions. The dataset is originated from the ATLAS experiment, and we anticipate that this approach can be seamlessly integrated into the ATLAS system. This development brings a significant improvement compared to the deployed GANs by ATLAS and could offer great enhancement to the current ATLAS fast simulations.
Michele Faucci Giannelli and Rui Zhang have contributed equally to this work.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
