A Neural Network Approach for Orienting Heavy-Ion Collision Events
A Neural Network Approach for Orienting Heavy-Ion Collision Events
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DOI：
10.1016/j.physletb.2023.138359
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发表时间：
2023-08
影响因子：
4.4
通讯作者：
Zu-Xing Yang;Xiao-Hua Fan;Zhi-Pan Li;S. Nishimura
中科院分区：
文献类型：
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作者：
Zu-Xing Yang;Xiao-Hua Fan;Zhi-Pan Li;S. Nishimura
一个基于卷积神经网络的分类器，通过整合多个典型的实验观测值来追溯变形的核-核碰撞的初始方向。本文采用同位旋相关的Boltzmann-Uehling-Uhlenbeck输运模型，计算了E束能量为1GeV/核子时铀-铀超中心碰撞的随机取向数据。从统计学上讲，数据驱动的极化方案基本上是通过分类器完成的，其不同的类别过滤掉特定的方向偏置的碰撞事件。这将促进基于形变核的核对称能、中子皮等研究。
A convolutional neural network-based classifier is elaborated to retrace the initial orientation of deformed nucleus-nucleus collisions by integrating multiple typical experimental observables. The isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model is employed to generate data for random orientations of ultra-central uranium-uranium collisions at E beam= 1 GeV/nucleon. Statistically, the data-driven polarization scheme is essentially accomplished via the classifier, whose distinct categories filter out specific orientation-biased collision events. This will advance the deformed nucleus-based studies on nuclear symmetry energy, neutron skin, etc.