A Living Review of Machine Learning for Particle Physics
This review provides a centralized and up-to-date resource for researchers in particle physics seeking to apply or develop machine learning techniques, addressing the challenge of keeping pace with rapid advancements.
This paper presents a living review of machine learning applications in high energy physics, compiling a comprehensive list of citations for researchers in experimental, phenomenological, and theoretical analyses. It aims to be continuously updated to reflect the rapid advancements in the field.
Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.