arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2301.06396nucl-thnucl-ex
收藏

中低能核物理中的机器学习

Machine learning in nuclear physics at low and intermediate energies

Wanbing He, Qingfeng Li, Yugang Ma, Zhongming Niu, Junchen Pei, Yingxun Zhang

摘要:

机器学习作为一种面向大数据处理任务的建模工具,其强大的建模能力使其成为各个研究领域科学研究的新范式。在这篇小综述中,我们首先简要介绍了机器学习算法和技术的不同方法。作为机器学习应用的一个缩影,本文重点介绍了机器学习在中低能核物理中的一些应用,包括在原子核结构、核反应、核物质性质等方面的理论应用,以及在事件识别/重构、复杂系统控制和固件性能等方面的实验应用。最后,我们还对机器学习在中低能核物理中应用的可能方向以及ML算法可能的改进进行了简要的总结和展望。

英文摘要:

Machine learning is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing task. In this mini-review, we first briefly introduce different methodologies of the machine learning algorithms and techniques. As a snapshot of many applications by machine learning, some selected applications are presented especially for low and intermediate energy nuclear physics, which include topics on theoretical applications in nuclear structure, nuclear reactions, properties of nuclear matter as well as experimental applications in event identification/reconstruction, complex system control and firmware performance. Finally, we also give a brief summary and outlook on the possible directions of using machine learning in low-intermediate energy nuclear physics and possible improvements in ML algorithms.

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。

↑