摘要
随着高能核物理研究进入多维度、高复杂度数据分析阶段,深度学习技术正逐步成为理解极端条件下核物质行为的关键工具,并推动研究范式从经验驱动向数据驱动的根本转变。本文简要梳理了机器学习在该领域的演进,并着重介绍了深度学习方法在其中的前沿进展:早期(20世纪末至21世纪10年代)研究主要采用人工神经网络和支持向量机等传统算法,通过核质量预测、相变识别等任务验证了机器学习处理核物理问题的可行性,但受限于人工特征提取和计算能力的制约,尚未触及物理特征的自主挖掘;深度学习时代(21世纪10年代至今),研究者创新性地引入点云网络架构,通过直接处理末态粒子四动量数据,不仅突破了传统方法依赖人工构造统计观测量的局限,更开启了从数据表象到物理实在认知跃迁的进程。与此同时,无监督学习方法推动研究重心从假设验证转向数据驱动的物理规律自主发现,不仅实现了异常信号的敏锐捕捉,更催生出物理现象涌现性研究的新思路。展望未来,从发展包含物理先验的深度学习算法以提升模型的物理含义,到元学习与自监督框架深化稀有事件分析;从量子机器学习加速提取高维数据特征,到生成式模型重构物理数据生态,这些发展或将推动高能核物理从观测数据的被动解释转向物理规律的主动发现,从局部特征的碎片化分析转向系统行为的整体性认知,最终有可能构建具有自主知识发现能力的智能物理研究体系。
As high-energy nuclear physics research enters a phase characterized by multi-dimensional and highly complex data analysis,deep learning techniques are gradually becoming essential tools for understanding nuclear matter behavior under extreme conditions.This shift is driving a fundamental transformation in research paradigms from experience-driven approaches toward data-driven methodologies.This article briefly reviews the evolution of machine learning in this field,emphasizing recent advancements involving deep learning techniques.Early research(from the late 20th century to the 2010s)primarily employed traditional algorithms such as artificial neural networks and support vector machines.These studies validated the feasibility of machine learning approaches in nuclear physics through tasks like nuclear mass prediction and phase transition identification.However,due to limitations in manual feature extraction and computational capabilities,such methods did not yet extend to autonomous exploration of physical features.In the deep learning era(2010s to present),researchers have innovatively introduced point-cloud neural network architectures,enabling direct processing of final-state particle four-momentum data.This advancement has overcome the constraints of traditional methods that relied heavily on manually constructed statistical observables and initiated a conceptual leap from superficial data representations toward intrinsic physical insights.Simultaneously,unsupervised learning methods have shifted research focus from hypothesis validation to autonomous,data-driven discovery of physical laws,facilitating not only sensitive detection of anomalous signals but also opening new avenues for investigating emergent physical phenomena.Looking ahead,from developing deep learning algorithms incorporating physical priors to enhance the model physical interpretation,to meta-learning and self-supervised frameworks deepening rare event analysis;from quantum machine learning accelerating highdimensional feature extraction,to generative models reconstructing the physical data ecosystem,these advancements will potentially propel high-energy nuclear physics research from the passive interpretation of observational data toward active discovery of physical laws,shifting analysis from fragmented,local feature exploration toward holistic comprehension of systemic behaviors.Ultimately,this progression may pave the way toward constructing an intelligent physics research system capable of autonomous knowledge discovery.
作者
张靖宗
郭爽
朱励霖
王凌霄
马国亮
ZHANG Jingzong;GUO Shuang;ZHU Lilin;WANG Lingxiao;MA Guoliang(Department of Physics,Sichuan University,Chengdu 610064,China;Key Laboratory of Nuclear Physics and Ion-beam Application(MOE),Institute of Modern Physics,Fudan University,Shanghai 200433,China;Shanghai Research Center for Theoretical Nuclear Physics,NSFC and Fudan University,Shanghai 200438,China;Interdisciplinary Theoretical and Mathematical Sciences Program(iTHEMS),RIKEN,Wako,Saitama 351-0198,Japan)
出处
《核技术》
北大核心
2025年第5期106-115,共10页
Nuclear Techniques
基金
国家自然科学基金(No.12147101,No.12325507)
国家重点研发计划(No.2022YFA1604900)
广东省基础与应用基础研究重大项目(No.2020B0301030008)资助。
关键词
机器学习
深度学习
重离子碰撞
科学智能
Machine learning
Deep learning
Heavy-ion collisions
AI for science
