Modern Machine Learning and Particle Physics Phenomenology at the LHC

Abstract

Modern machine learning is driving a paradigm shift in particle physics phenomenology at the Large Hadron Collider. This short review examines the transformative role of machine learning across the entire theoretical prediction pipeline, from parton-level calculations to full simulations. We discuss applications to scattering amplitude computations, phase space integration, Parton Distribution Function determination, and parameter extraction. Some critical frontiers for the field including uncertainty quantification, the role of symmetries, and interpretability are highlighted.

Comments: Contribution to EuCAIFCon 2025 (https://inspirehep.net/conferences/2832076?ui-citation-summary=true) Summary talk, 13 pages