Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

作者：X. Zhang, X. Liu, Y. Huang, Weiping Lin, H. Zheng, R. Wada, A. Bonasera, Z. Chen, Liyu Chen, Jifeng Han, R. Han, M. Huang, Qiang Hu, Quanyang Leng, Chun-Wang Ma, Guofeng Qu, P. Ren, G. Tian, Xu Zhao, Zu-Xing Yang, L. Zhang · 发表于：Physical Review C · 年份：2022 · DOI：10.1103/physrevc.105.034611 · 被引用次数：19 · 研究领域：Nuclear physics research studies、Nuclear reactor physics and engineering、Nuclear Physics and Applications

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact-parameter-sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher...