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arXiv 2303.15136hep-phastro-ph.HEhep-exhep-latnucl-th
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利用机器学习探索极端条件下的QCD物质

Exploring QCD matter in extreme conditions with Machine Learning

Kai Zhou, Lingxiao Wang, Long-Gang Pang, Shuzhe Shi

备注：
152 pages,53 figures
发表信息：
Progress in Particle and Nuclear Physics, 104084, 2023
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AI总结：

本文综述了机器学习在重离子碰撞、格点场论和中子星等高能核物理理论研究中的应用，强调从数据驱动向物理驱动方法演进并融入物理先验的重要性。

摘要：

近年来，机器学习已成为物理学中一种强大的计算工具和新颖的问题求解视角，为研究极端条件下强相互作用QCD物质的性质提供了新途径。本综述旨在概述这一交叉领域的现状，重点关注机器学习在高能核物理理论研究中的应用。文章涵盖多个方面，包括重离子碰撞、格点场论和中子星，并讨论如何利用机器学习探索和促进理解QCD物质的物理目标。本综述还从方法论角度提供了共性概述，涵盖从数据驱动视角到物理驱动视角的演进。最后，我们讨论了机器学习在高能核物理中应用的挑战与未来前景，并强调将物理先验纳入纯数据驱动学习工具箱的重要性。本综述凸显了机器学习作为推进高能核物理探索的宝贵计算范式的关键作用。

英文摘要：

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.

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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