Buch ; Online: Optimizing Geant4 Hadronic Models
2023
Abstract: 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 ... ...
| Abstract | 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. Comment: 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP 2023) |
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| Schlagwörter | High Energy Physics - Experiment ; Physics - Instrumentation and Detectors |
| Thema/Rubrik (Code) | 621 |
| Erscheinungsdatum | 2023-09-21 |
| Erscheinungsland | us |
| Dokumenttyp | Buch ; Online |
| Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
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