Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high energy physics but not machine learning. The connections between machine learning and high energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

中文翻译：

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深度学习及其在 LHC 物理中的应用

几十年来，机器学习在分析高能物理数据方面发挥了重要作用。2012 年深度学习的出现使得机器学习工具能够熟练地处理比以前可行的更高维和更复杂的问题。本综述面向熟悉高能物理但不熟悉机器学习的读者。探索机器学习和高能物理数据分析之间的联系，然后介绍神经网络的核心概念，展示深度学习对 LHC 数据分析能力的关键结果示例，以及未来前景和关注点的讨论.