Over the last decade, machine learning algorithms have become standard analysis tools in astroparticle physics, used by a variety of instruments and for an even larger variety of analyses. While a few characteristic patterns can be observed, the portability of established machine learning-based analysis chains from one experiment to another, remains challenging, as instrument-specific prerequisites and adjustments need to be addressed prior to the application. The use Boosted Decision Trees and other tree-based ensemble methods, has been established, but also recently been challenged by the overall success of Deep Neural Networks. Machine learning has been applied for particle selection and parameter reconstruction, as well as for the extraction of energy spectra. This paper aims at summarizing some of the most common approaches on the application of machine learning in astroparticle physics and at providing brief overview on how they have been applied in practice.

中文翻译：

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机器学习算法在切伦科夫成像和中微子天文学中的应用

在过去十年中，机器学习算法已成为天体粒子物理学中的标准分析工具，被各种仪器使用，并用于更多种类的分析。虽然可以观察到一些特征模式，但已建立的基于机器学习的分析链从一个实验到另一个实验的可移植性仍然具有挑战性，因为在应用之前需要解决特定于仪器的先决条件和调整。使用 Boosted Decision Trees 和其他基于树的集成方法已经建立，但最近也受到深度神经网络整体成功的挑战。机器学习已被应用于粒子选择和参数重建，以及能量谱的提取。