Machine learning for beam dynamics studies at the CERN Large Hadron Collider

TL;DR

This paper reviews recent applications of machine learning techniques to beam dynamics studies at CERN's LHC, highlighting advancements in data analysis, performance optimization, and simulation interpretation in accelerator physics.

Contribution

It provides a comprehensive overview of how machine learning is being applied to LHC beam dynamics, including current methods and future prospects, marking a significant step forward in accelerator physics research.

Findings

Machine learning improves beam measurement accuracy.

Enhanced performance optimization at LHC using ML techniques.

ML aids in analyzing complex non-linear beam dynamics simulations.

Abstract

Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments.