A Living Review of Machine Learning for Particle Physics

TL;DR

This paper presents a continuously updated review of machine learning applications in particle physics, aiming to serve as a comprehensive resource for researchers in the rapidly evolving field.

Contribution

It introduces a living review that compiles and updates citations of machine learning methods used in high energy physics research, facilitating community engagement.

Findings

Extensive list of ML applications in particle physics

Regular updates to include latest research developments

Community contributions encouraged for ongoing curation

Abstract

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.