TY  - JOUR
A1  - Wang, Yongjia
A1  - Li, Fupeng
A1  - Li, Qingfeng
A1  - Lü, Hongliang
A1  - Zhou, Kai
T1  - Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
T2  - Physics Letters B
N2  - A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) 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 labeled 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 Esym(ρ) 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 accuracies for 5-label (5 different Esym(ρ)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of Esym(ρ)) 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.
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/78026
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-780269
SN  - 0370-2693
VL  - 822
IS  - 136669
PB  - Elsevier
CY  - Amsterdam
ER  -