QCD Masterclass Lectures on Jet Physics and Machine Learning

TL;DR

This paper reviews machine learning principles in collider physics, focusing on jet discrimination, and introduces novel parametrized likelihood ratios for quark-gluon separation using multiple observables.

Contribution

It presents a comprehensive review of ML applications in jet physics and introduces new parametrized likelihood expressions for quark-gluon discrimination.

Findings

Parametrized likelihood ratio for quark-gluon discrimination with multiple observables.

Analysis of binary discrimination techniques in jet physics.

End-of-lecture exercises for practical understanding.

Abstract

These lectures were presented at the 2024 QCD Masterclass in Saint-Jacut-de-la-Mer, France. They introduce and review fundamental theorems and principles of machine learning within the context of collider particle physics, focused on application to jet identification and discrimination. Numerous examples of binary discrimination in jet physics are studied in detail, including $H\to b\bar b$ identification in fixed-order perturbation theory, generic one-versus two-prong discrimination with parametric power counting techniques, and up versus down quark jet classification by assuming the central limit theorem, isospin conservation, and a convergent moment expansion of the single particle energy distribution. Quark versus gluon jet discrimination is considered in multiple contexts, from using additive, infrared and collinear safe observables, to using hadronic multiplicity, and to including measurements of the jet charge. While many of the results presented here are well known, some novel results are presented, the most prominent being a parametrized expression for the likelihood ratio of quark versus gluon discrimination for jets on which hadronic multiplicity and jet charge are simultaneously measured. End-of-lecture exercises are also provided.