利用机器学习解决重离子碰撞中的关键问题  

Key Problems to Be Solved Possibly Using Machine Learning in Heavy Ion Collisions

作　　者：邹盈 张钰海 唐娜 李静静 张丰收[1,2,3] ZOU Ying;ZHANG Yuhai;TANG Na;LI Jingjing;ZHANG Fengshou(Key Laboratory of Beam Technology of Ministry of Education,College of Nuclear Science and Technology,Beijing Normal University,Beijing 100875,China;Institute of Radiation Technology,Beijing Academy of Science and Technology,Beijing 100875,China;Center of Theoretical Nuclear Physics,National Laboratory of Heavy Ion Accelerator of Lanzhou,Lanzhou 730000,China)

机构地区：[1]北京师范大学核科学与技术学院、射线束技术教育部重点实验室,北京100875 [2]北京市科学技术研究院、辐射技术研究所,北京100875 [3]兰州重离子加速器国家实验室、原子核理论研究中心,甘肃兰州730000

出　　处：《原子能科学技术》2023年第4期762-773,共12页Atomic Energy Science and Technology

基　　金：国家自然科学基金(12135004,11635003,11961141004,12105019,12047513)。

摘　　要：本文介绍了当前用于重离子碰撞研究几种代表性的输运理论模型,并以应用它们进行的计算结果为例介绍输运理论对重离子碰撞的研究工作进展。对目前输运理论面对的一些困难以及机器学习在重离子碰撞输运理论中的应用现状进行总结,提出了输运理论中有待机器学习方法解决的几点关键问题:建立Boltzmann方程的基本假设导致丢失核子间关联;Boltzmann方程类型仅考虑两体碰撞而忽略了多体碰撞的特点不符合高密度问题下的实际物理情况;QMD类型对量子效应的考虑不够精确;采用多时间尺度处理从剧烈碰撞阶段到达到局域平衡问题;模型输入相关物理量对应的实验观测数据不足。Heavy-ion collisions provide a unique opportunity to study the nuclear equation of state for a wide range of densities,temperatures,and neutron-proton asymmetries in the laboratory.Transport models are the main method to obtain physics information on the nuclear equation of state and in-medium properties of particles from low to relativistic-energy heavy-ion collisions.In this paper,the typical transport models used in heavy ion collisions were introduced,including the phenomenology-type transport model,such as dinuclear system(DNS)model and Langevin equation,Boltzmann-equation and quantum molecular dynamics(QMD)type transport model.Besides,some of their computed results were taken as examples to introduce the progress of transport theory.The difficulties faced by the current transport model were also summarized,and the application of machine learning in heavy ion collisions transport theory was reviewed.In this area,machine learning,especially deep learning,has been widely used as a significant data analysis method.Machine learning could be applied to correct the input of transport model,such as the nuclear equation of state and impact parameter,and constrain the output results,such as fission yields.The convolutional neural networks(CNN),light gradient boosting machine(LightGBM),Bayesian neural network(BNN)were introduced in these studies mentioned.Based on the above,the key problems to be solved possibly using machine learning in transport theory were proposed:Nucleon-nucleon correlations are not included in Boltzmann-type transport model,due to the basic assumptions needed to derive Boltzmann equation from Bogoliubov-Born-Green-Kirkwood-Yvon(BBGKY)equations,which are Rarefied gas hypothesis and Stosszahlansatz;Only two-body collision is considered in Boltzmann equation without N-body collisions,which is unphysical in high density.High order terms of BBGKY equations need to be considered;The fermionic nature of the system should be preserved in the evolution of nuclear reaction system,but is lost rapidly in QM

关 键 词：唯象类输运理论 Boltzmann方程类型输运理论 QMD类型输运理论 重离子碰撞 机器学习 

分 类 号：O571.6[理学—粒子物理与原子核物理]