Machine Learning in High Energy Physics Community White Paper

TL;DR

This white paper reviews the history and future potential of machine learning in high energy physics, emphasizing research directions, resource needs, and collaborative efforts to enhance physics analysis at major experiments.

Contribution

It provides a comprehensive roadmap for integrating machine learning into particle physics research, including implementation strategies, resource requirements, and collaboration opportunities.

Findings

Machine learning has significantly advanced particle identification and reconstruction.

Future research will focus on integrating ML with high-luminosity collider data.

Collaborations with data science communities are essential for progress.

Abstract

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.