Machine Learning in the Search for New Fundamental Physics

TL;DR

This paper reviews how modern machine learning, especially deep learning, has transformed the search for new fundamental physics in high energy experiments like the LHC, rare events, and neutrino research.

Contribution

It provides a comprehensive overview of recent machine learning methods and their impact on advancing fundamental physics searches.

Findings

Deep learning has significantly expanded research scope.

Machine learning improves detection sensitivity.

Modern methods accelerate physics discovery.

Abstract

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.