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arXiv 2203.01246hep-phhep-exhep-thnucl-exnucl-th
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基于深度学习的重离子碰撞椭圆流系数估计

Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning

Neelkamal Mallick, Suraj Prasad, Aditya Nath Mishra, Raghunath Sahoo, Gergely Gábor Barnaföldi

备注：
Same as the published version in Phys. Rev. D
发表信息：
Physical Review D 105, 114022 (2022)
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摘要：

自80年代初以来，机器学习（ML）技术一直用于高能物理（HEP）社区，以处理广泛的问题。这项工作探讨了使用深度学习技术来估计RHIC和LHC能量下重离子碰撞中的椭圆流（$v_2$）的前景。提出了一种从粒子运动学信息中处理输入观测量的新方法。用AMPT模型模拟的$\sqrt{s_{\rm NN}} = 5.02$ TeV的Pb-Pb碰撞事件训练DNN模型。ML技术的预测进行了比较，模拟和实验。深度学习模型似乎保留了$v_2$对LHC和RHIC能量的中心性和能量依赖性。DNN模型在预测$v_2$的$p_{\rm T}$依赖性方面也相当成功。当受到额外噪声的事件模拟时，所提出的DNN模型仍然在合理的程度上保持了鲁棒性和预测精度。

英文摘要：

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.

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。

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