人工智能在确定中能重离子碰撞参量中的应用
Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
摘要：
碰撞参量是重离子碰撞（HIC）的重要物理量之一，对末态的多重碎裂和集体流等观测量有着重要的影响。通常，它不能在实验中直接测量，但可以从最终状态的可观测值中推断出来。人工智能在学习复杂的数据表示方面取得了巨大的成功，这使得物理科学中的新建模和数据处理方法成为可能。本文采用人工智能领域中两种常用的算法--卷积神经网络（CNN）和光梯度增强机（LightGBM），通过逐事件分析质子的横动量谱和快度谱，提高了确定碰撞参量的精度。用超相对论量子分子动力学（UrQMD）模型模拟了中能区（$E_{\rm lab}$ = $0.2$ - $1.0$ $/$ GeV核子）碰撞参量为0 $\leq$ $b$ $\leq$ 10 fm的Au+Au碰撞过程，得到了质子能谱数据.结果表明，真实的碰撞参量与估计的撞击参数之间的平均差值可以小于0.1 fm。LightGBM算法在这项工作中的任务上显示出相对于CNN的改进性能。利用LightGBM的可视化算法，可以得到横动量和快度分布的重要特征图，这对重离子实验中碰撞参量或中心性的推断有一定的帮助。
英文摘要：
The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the Convolutional Neural Networks (CNN) and Light Gradient Boosting Machine (LightGBM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au+Au collisions with the impact parameter of 0$\leq$$b$$\leq$10 fm at intermediate energies ($E_{\rm lab}$=$0.2$-$1.0$ GeV$/$nucleon) are simulated with the ultrarelativistic quantum molecular dynamics (UrQMD) model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightGBM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightGBM's visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
