Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
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|
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Article Number | 03026 | |
Number of page(s) | 7 | |
Section | Offline Computing | |
DOI | https://doi.org/10.1051/epjconf/202429503026 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429503026
Optimizing Geant4 Hadronic Models
1 Fermi National Accelerator Laboratory, Batavia, IL, 60510-5011, USA
2 CERN 27210, CH-1211 Geneva, Switzerland
3 Joint Institute for Nuclear Research, Dubna, Moscow Region, Russia 141 980
* e-mail: yarba_j@fnal.gov
Published online: 6 May 2024
Geant4, the leading detector simulation toolkit used in high energy physics, employs a set of physics models to simulate interactions of particles with matter across a wide range of energies. These models, especially the hadronic ones, rely largely on directly measured cross-sections and inclusive characteristics, and use physically motivated parameters. However, they generally aim to cover a broad range of possible simulation tasks and may not always be optimized for a particular process or a given material. The Geant4 collaboration recently made many parameters of the models accessible via a configuration interface. This opens a possibility to fit simulated distributions to the thin target experimental datasets and extract optimal values of the model parameters and the associated uncertainties. Such efforts are currently undertaken by the Geant4 collaboration with the goal of offering alternative sets of model parameters, also known as "tunes", for certain applications. The effort should subsequently lead to more accurate estimates of the systematic errors in physics measurements given the detector simulation role in performing the physics measurements. Results of the study are presented to illustrate how Geant4 model parameters can be optimized through applying fitting techniques, to improve the agreement between the Geant4 and the experimental data.
© The Authors, published by EDP Sciences, 2024
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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