利用机器学习研究中低能重离子碰撞中的物理问题  

Study of Physics Problem in Low-intermediate Energy Heavy Ion Collision with Machine Learning Algorithm

作　　者：张英逊[1,2] 王方元 李理 陈响 崔莹 王馨钰 杨钧评 赵凯 ZHANG Yingxun;WANG Fangyuan;LI Li;CHEN Xiang;CUI Ying;WANG Xinyu;YANG Junping;ZHAO Kai(Department of Nuclear Physics,China Institute of Atomic Energy,Beijing 102413,China;Guangxi Key Laboratory of Nuclear Physics and Technology,Guangxi Normal University,Guilin 541004,China)

机构地区：[1]中国原子能科学研究院核物理研究所,北京102413 [2]广西师范大学、广西核物理与核技术重点实验室,广西桂林541004

出　　处：《原子能科学技术》2023年第4期751-761,I0001,共12页Atomic Energy Science and Technology

基　　金：中国原子能科学研究院院长基金(YZ222407001301);国家自然科学基金(11875323,12275359,11961141003);国家财政部稳定支持研究经费(WDJC-2019-13);国家重点研发计划(2018YFA0404404);中核集团领创项目(LC192209000701,LC202309000201)。

摘　　要：中低能重离子碰撞的内禀涨落破坏了重离子碰撞的初态输入量与末态观测量的一一对应关系,从而对利用机器学习从末态观测量反推感兴趣的初态输入物理量,如碰撞参数、状态方程等提出了新的挑战。本文从微观动力学输运模型出发,分析了末态观测量相对于初态输入量产生分布的原因。理论计算表明末态观测量对于初态输入量的涨落近似满足高斯形式。通过结合贝叶斯定理和无监督的机器学习算法,可以模型无关地分类碰撞的事件以及重构碰撞参数的分布。进一步利用两种神经网络对质子、中子在同位旋非对称介质中有效质量的劈裂进行了分析,指出末态实验数据的扁平化处理能提高卷积神经网络和简单神经网络分辨质子、中子有效质量劈裂的精度。The medium effects of nucleons and equation of state(EOS)are two important issues in low-intermediate energy nuclear physics.In laboratory,the medium effects of nucleons and EOS can be learned by comparing the heavy ion collisions(HICs)with the transport model simulations.Up to now,the main challenges are to find sensitive probes for learning the medium effects of nucleons,such as the effective mass for proton and neutron,and to improve the reliability of the extraction of the medium effects of nucleons and EOS by comparing the transport model calculations with experimental data.To improve the reliability of the comparisons,it is necessary to use the same conditions as those used in experiments,such as angle cut or energy bins for measured particles or fragments,and centrality of the reaction.The angle cut and energy bins can be easily realized in the calculations.However,the centrality of HICs can not be used directly as an input in the simulations,since the centrality of HICs does not correspond to a specific value of the impact parameter.Another,the impact parameter used in the transport model simulations can not be directly measured in experiments.Thus,reconstructing the impact parameter distribution from the centrality of HIC observables is necessary for simulating HICs and comparing with data in a reasonable way.In this work,we first investigate the fluctuation mechanism in low-intermediate energy HICs.Our calculations show that the initial statistical fluctuations and random nucleon-nucleon scattering are two origins of inherent fluctuations.The fluctuation mechanism leads the observables to be distributed over a wide range even with the same inputs,such as a fixed value of impact parameter,beam energy,EOS,nucleon-nucleon scattering cross section,etc.On the other hand,a fixed or certain value of observables maps to the input variables over a wide range.If a machine learning is used to map the values of observables to the distribution of input physics variables,a novel algorithm should be proposed.For model

关 键 词：重离子碰撞 输运理论模型 涨落机制 碰撞参数 有效质量劈裂 机器学习算法 

分 类 号：O571.6[理学—粒子物理与原子核物理]