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QCD Masterclass Lectures on Jet Physics and Machine Learning by Larkoski, Andrew J. is a scholarly article available to read on EtoBox.

What is QCD Masterclass Lectures on Jet Physics and Machine Learning about?

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 simul

Author

Larkoski, Andrew J.

Published

2024

Language

EN