Sep 9 – 14, 2024
Caen
Europe/Paris timezone

Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning

Sep 13, 2024, 9:30 AM
20m
GANIL Guest House (Caen)

GANIL Guest House

Caen

Oral Presentation Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

Speaker

Yongjia Wang (Huzhou University)

Description

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are used in these approaches, however, observables can be obtained event-by-event both in experiments and transport model simulations. By using the light gradient boosting machine (LightGBM), a modern machine-learning algorithm, we present a framework for inferring the density-dependent nuclear symmetry energy from observables in heavy-ion collisions on the event-by-event analysis. The ultrarelativistic quantum molecular dynamics (UrQMD) model simulations are used as training data. The symmetry energy slope parameter extracted with LightGBM event-by-event from test data also by UrQMD has an average spread of approximately 30 MeV from the truth, and is found to be robust against variations in model parameters. In addition, LightGBM can identify features that have the greatest effect on the physics of interest, thereby offering valuable insights. Our study suggests that the present framework can be a powerful tool and may offer a new paradigm to study the underlying physics in heavy-ion collisions.

Primary author

Yongjia Wang (Huzhou University)

Presentation materials