A review on machine learning for neutrino experiments
A review on machine learning for neutrino experiments
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DOI：
10.1142/s0217751x20430058
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发表时间：
2020-08
影响因子：
1.6
通讯作者：
F. Psihas;M. Groh;C. Tunnell;K. Warburton
中科院分区：
文献类型：
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作者：
F. Psihas;M. Groh;C. Tunnell;K. Warburton
中微子实验通过观察它们与物质的直接相互作用或寻找超罕见信号来研究标准模型中最不为人所知的粒子。中微子的研究通常需要克服大的背景、难以捉摸的信号和小的统计数据。引入最先进的机器学习工具来解决分析任务，对中微子实验中的这些挑战产生了重大影响。机器学习算法已经成为中微子物理研究中不可或缺的工具，其发展对下一代实验的能力具有重要意义。理解这些技术在应用中仍然存在的障碍，无论是人为的还是计算的，以及挑战，对于它们在物理应用中的正确和有益的利用至关重要。这篇综述介绍了机器学习应用于中微子物理的现状，以及这两个领域之间的挑战和机遇。
Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. The introduction of state-of-the-art machine learning tools to solve analysis tasks has made major impacts to these challenges in neutrino experiments across the board. Machine learning algorithms have become an integral tool of neutrino physics, and their development is of great importance to the capabilities of next generation experiments. An understanding of the roadblocks, both human and computational, and the challenges that still exist in the application of these techniques is critical to their proper and beneficial utilization for physics applications. This review presents the current status of machine learning applications for neutrino physics in terms of the challenges and opportunities that are at the intersection between these two fields.