The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.
翻译:鉴于高能物理学的核心是复杂的探测器、大型数据、数据集和尖端分析,利用物理数据固有的对称性激发了物理学知情的ML,作为充满活力的计算机科学研究的次领域,高能物理学和机器学习(ML)在高能物理学(HEP)中的作用日益增强,这种作用已经牢固确立,而且具有相关性,此外,高能物理学和机器学习(ML)研究人员从广泛提供的材料中受益匪浅,用于教育、培训和劳动力发展,他们也为这些材料作出贡献,并向DS/ML相关领域提供软件。物理部门越来越多地在DS、ML和物理学的交叉点提供课程,这些课程经常使用高能物理学研究人员开发的课程,并涉及HEP中使用的开放软件和数据。在本白皮书中,我们探讨了高能研究与DS/ML教育之间的协同作用,讨论这一交叉点上的机会和挑战,并提出互利的社区活动。