Systematic optimization of resonance parameters in a transport approach

Abstract

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.