Jet Physics from the Ground Up

TL;DR

This paper provides an overview of jet physics in quantum chromodynamics, covering theoretical foundations, effective descriptions, and the role of machine learning in jet analysis, with educational exercises included.

Contribution

It introduces a simplified effective description of QCD jets and discusses the application of machine learning for jet discrimination from a biased perspective.

Findings

Likelihood for quark vs. gluon discrimination is infrared and collinear safe.

Effective description of QCD jets developed from simple assumptions.

Machine learning enhances human understanding of jet physics.

Abstract

These are lecture notes presented at the online 2021 QUC Winter School on Energy Frontier hosted by the Korea Institute for Advanced Study. They extend lectures presented at the 2017 and 2018 CTEQ summer schools and the 2020 Hadron Collider Physics Summer School hosted by Fermilab. Jets in quantum chromodynamics (QCD) are motivated from familiar results in classical electricity and magnetism, through identification of structures that exhibit approximate scale invariance. From this point, an effective description of QCD jets is developed from simple assumptions that necessitate all-orders resummation for obtaining finite results. With machine learning becoming an increasingly important tool of particle physics, I discuss its utility exclusively from the biased view for increasing human knowledge. A simple argument that the likelihood for quark versus gluon discrimination is infrared and collinear safe is presented as an example of this approach. End-of-lecture exercises are also provided.