Modern Machine Learning and Particle Physics Phenomenology at the LHC

TL;DR

This paper reviews how modern machine learning techniques are revolutionizing particle physics phenomenology at the LHC, impacting various stages from calculations to data analysis and highlighting key challenges.

Contribution

It provides a comprehensive overview of machine learning applications in particle physics, emphasizing recent advances and future frontiers in the field.

Findings

Machine learning enhances scattering amplitude computations.

ML improves phase space integration and PDF determination.

Uncertainty quantification and interpretability are critical challenges.

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.