Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning

TL;DR

This paper employs deep learning to identify signatures of nuclear symmetry energy in heavy-ion collision data, achieving high accuracy in classifying different symmetry energy models and estimating related parameters.

Contribution

It introduces a CNN-based method to analyze proton and neutron spectra for extracting nuclear symmetry energy information from heavy-ion collisions.

Findings

Event-summed proton spectra yield 98% classification accuracy.

Neutron spectra provide better regression accuracy with MAE of 14.8 MeV.

CNN effectively identifies symmetry energy effects in collision data.

Abstract

A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labelled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff $E_{\rm sym}(\rho)$ is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracy for 5-label (5 different $E_{\rm sym}(\rho)$) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute error (MAE) which measures the average magnitude of the absolute differences between the predicted and actual $L$ (the slope parameter of $E_{\rm sym}(\rho)$) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm.