Machine Learning in the Search for New Fundamental Physics
It addresses the problem of improving efficiency and scope in fundamental physics research for scientists in high-energy physics, but it is incremental as it reviews existing developments.
This paper reviews how machine learning, particularly deep learning since the early 2010s, enhances and accelerates searches for new fundamental physics in high-energy experiments like the Large Hadron Collider, rare event searches, and neutrino experiments.
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.