论文标题
使用深度学习估算重离子碰撞中的椭圆流系数
Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning
论文作者
论文摘要
自80年代初以来，已经针对高能量物理（HEP）社区采用了机器学习（ML）技术来处理广泛的问题。这项工作探讨了使用深度学习技术在RHIC和LHC Energies的重离子碰撞中估算椭圆流（$ v_2 $）的前景。开发了一种新的方法来处理粒子运动学信息的输入可观察物。提出的DNN模型在$ \ sqrt {s _ {\ rm nn}}} = 5.02 $ tev最小偏差事件中，用$ \ sqrt {s _ {s _ {\ rm nn}}培训了DNN模型。将ML技术的预测与模拟和实验进行了比较。深度学习模型似乎可以保留LHC和Rhic Energies $ v_2 $的中心性和能量依赖性。 DNN模型在预测$ v_2 $的$ p _ {\ rm t} $依赖性方面也非常成功。当经过其他噪声进行事件模拟时，提出的DNN模型仍然可以使稳健性和预测准确性完好无损。
Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input observables from particle kinematic information. The proposed DNN model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with AMPT model. The predictions from the ML technique are compared to both simulation and experiment. The Deep Learning model seems to preserve the centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of $v_2$. When subjected to event simulation with additional noise, the proposed DNN model still keeps the robustness and prediction accuracy intact up to a reasonable extent.
