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.

本文讨论机器学习在粒子物理学研究中的应用前景和可行性，并详述实现机器学习在粒子物理学上开发所需的软硬资源、与数据科学社区、学术界和产业界的合作倡议以及对粒子物理学界在数据科学培训上进行的工作。旨在将研究和开发与物理驱动器连接起来，并确定实现它们的资源需求，以及其他社区的合作领域。