Machine Learning in High Energy Physics Community White Paper

Abstract

Machine learning has been applied to several problems in particle physics\nresearch, beginning with applications to high-level physics analysis in the\n1990s and 2000s, followed by an explosion of applications in particle and event\nidentification and reconstruction in the 2010s. In this document we discuss\npromising future research and development areas for machine learning in\nparticle physics. We detail a roadmap for their implementation, software and\nhardware resource requirements, collaborative initiatives with the data science\ncommunity, academia and industry, and training the particle physics community\nin data science. The main objective of the document is to connect and motivate\nthese areas of research and development with the physics drivers of the\nHigh-Luminosity Large Hadron Collider and future neutrino experiments and\nidentify the resource needs for their implementation. Additionally we identify\nareas where collaboration with external communities will be of great benefit.\n