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.

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中微子实验机器学习综述

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稀有信号来研究对标准模型粒子了解最少的情况。中微子的研究通常需要克服大背景、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。机器学习算法已成为中微子物理学不可或缺的工具，其发展对下一代实验的能力具有重要意义。了解人类和计算方面的障碍，以及这些技术应用中仍然存在的挑战，对于它们在物理应用中的正确和有益利用至关重要。