Systematic optimization of resonance parameters in a transport approach
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910863762391040 |
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| author | Rosenkvist, Carl B. Elfner, Hannah |
| author_facet | Rosenkvist, Carl B. Elfner, Hannah |
| contents | This study optimizes resonance parameters responsible for strangeness production in the SMASH (Simulating Many Accelerated Strongly-interacting Hadrons) transport model using a genetic algorithm. By fitting resonance parameters to experimental data on exclusive strangeness cross-sections at low energies, we significantly improve the model's accuracy, especially in pion-proton interactions. Our approach explores how machine learning tools can be used for precise resonance tuning in transport approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Systematic optimization of resonance parameters in a transport approach Rosenkvist, Carl B. Elfner, Hannah High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory This study optimizes resonance parameters responsible for strangeness production in the SMASH (Simulating Many Accelerated Strongly-interacting Hadrons) transport model using a genetic algorithm. By fitting resonance parameters to experimental data on exclusive strangeness cross-sections at low energies, we significantly improve the model's accuracy, especially in pion-proton interactions. Our approach explores how machine learning tools can be used for precise resonance tuning in transport approaches. |
| title | Systematic optimization of resonance parameters in a transport approach |
| topic | High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2503.05504 |