Deep Learning and its Application to LHC Physics

TL;DR

This paper reviews how deep learning techniques have advanced the analysis of LHC physics data, highlighting key results, challenges, and future directions in applying neural networks to high-energy physics research.

Contribution

It provides an accessible overview of deep learning applications in LHC physics, connecting machine learning concepts with high-energy physics data analysis.

Findings

Deep learning enhances analysis of complex LHC data.

Neural networks outperform traditional methods in certain tasks.

Future prospects include addressing challenges and expanding applications.

Abstract

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high energy physics but not machine learning. The connections between machine learning and high energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.