基于机器学习从逐事件重离子碰撞提取物理信息  

Event-by-event Extracting Physical Information from Heavy Ion Collisions Based on Machine Learning

作　　者：魏国俊 王永佳[2] 李庆峰[2] 刘福虎[1] WEI Guojun;WANG Yongjia;LI Qingfeng;LIU Fuhu(Institute of Theoretical Physics,Shanxi University,Taiyuan 030006,China;School of Science,Huzhou University,Huzhou 313000,China)

机构地区：[1]山西大学理论物理研究所,山西太原030006 [2]湖州师范学院理学院,浙江湖州313000

出　　处：《原子能科学技术》2023年第4期774-783,共10页Atomic Energy Science and Technology

基　　金：国家自然科学基金(U2032145,11875125,12147219);国家重点研发计划(2020YFE0202002)。

摘　　要：在重离子碰撞实验和输运模型模拟中均可逐事件获取观测量数据,然而,利用重离子碰撞研究核物质性质时通常只使用观测量对所有事件的平均值。机器学习拥有强大的数据分析和处理能力,可有效利用逐事件观测量数据中包含的丰富信息。本文通过极端相对论量子分子动力学(UrQMD)模型和机器学习算法的结合,为相关问题的研究提供新途径。研究结果显示,机器学习方法可从逐事件的观测量数据中提取关键物理参数的信息。此外,利用机器学习中特征量重要性归因方法,还可找出对提取相关参数最重要的特征量,从而对研究这些问题提供有价值的参考。The properties of nuclear matter at various densities is of great interest,as it is crucial for the understanding of the structure of nuclei and neutron stars,the dynamics of heavy ion collision(HIC),and neutron star mergers.HIC provides a unique opportunity to create nuclear matter with density away from the normal nuclear density(ρ0)in the terrestrial laboratory,however,the created nuclear matter only exists for a very short period and its properties cannot be measured directly.Usually,the properties of nuclear matter are deduced from the comparison of the event-average quantities between experimental measurements and transport model simulations.However,quantities in HIC can be obtained event-by-event both in experiments and in transport model simulations.These event-by-event data usually comprise a huge amount of data which encodes rich physical information,and at the same time,large fluctuations which can be taken as noises.Machine learning(ML)has been proven very powerful for the extraction of information from complex data in many branches of science.In this work,event-by-event observables from Au+Au and Sn+Sn collisions at intermediate energies are generated with the ultrarelativistic quantum molecular dynamics(UrQMD)model.Model parameters with different nuclear incompressibility K 0 of isospin symmetric nuclear matter,the slope parameter L of nuclear symmetry energy,and the in-medium correction factor F of nucleon-nucleon cross section were used to generate labeled dataset.The light gradient boosting machine(LightGBM)which is a decision-tree-based algorithm was used to learn the labeled dataset.The advantages of LightGBM include faster training efficiency,low memory usage,higher accuracy,and ability to tackle large-scale data.It is found that the trained machine learning algorithm is able to infer K 0 and L,and F from the event-by-event data.The mean absolute errors(MAE)which measure the average magnitude of the absolute differences between the predicted and true values of K 0 and L,and F are 52 MeV,29.6

关 键 词：机器学习 重离子碰撞 逐事件 

分 类 号：O571[理学—粒子物理与原子核物理]