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.
@article{arxiv.2503.05504,
title = {Systematic optimization of resonance parameters in a transport approach},
author = {Carl B. Rosenkvist and Hannah Elfner},
journal= {arXiv preprint arXiv:2503.05504},
year = {2025}
}