| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01354 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701354 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701354
Towards an experiment-independent toolkit for fast calorimeter simulation
From ATLAS to future HEP detectors
CERN
* e-mail: jbeirer@cern.ch
Published online: 7 October 2025
The production of a sufficiently large number of simulated Monte Carlo events is anticipated to be one of the most significant bottlenecks for many future high-energy physics (HEP) experiments. The simulation of the calorimeter response, in particular, represents a major computational challenge. While substantial efforts have been made by the HEP community to develop machine-learning based fast simulation models, integrating these into realistic experimental setups remains a significant hurdle.
Building on the fast simulation tools developed by the ATLAS Collaboration at the LHC, this paper presents recent efforts to create a fully experimentindependent library for fast calorimeter simulation. The library aims to provide a universal interface for both the lateral and longitudinal parameterization of calorimeter showers, as well as for machine-learning based approaches to shower generation.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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