Modern Machine Learning for LHC Physicists

TL;DR

This paper introduces modern machine learning techniques tailored for LHC physicists, emphasizing applications like classification and generative networks, to enhance data analysis and scientific discovery in particle physics.

Contribution

It provides an accessible overview of cutting-edge ML methods specifically adapted for LHC physics, including tutorials and practical examples for researchers.

Findings

Enhanced classification accuracy in LHC data analysis

Effective generative models for particle physics data

Improved uncertainty quantification in ML applications

Abstract

Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data. In any case, it is crucial for young researchers to stay on top of this development and apply cutting-edge methods and tools to all LHC physics tasks. These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications. They start with an LHC-specific motivation and a non-standard introduction to neural networks and then cover classification, unsupervised classification, generative networks, data representations, and inverse problems. Three themes defining much of the discussion are statistically defined loss functions, uncertainties, and accuracy. To understand the applications, the notes include some aspects of theoretical LHC physics. All examples are chosen from particle physics publications of the last few years, and many of them come with corresponding tutorials.