Jet Physics from the Ground Up: https://arxiv.org/abs/2112.15122
Andrew J. Larkoski
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.
Comments: 4 hour-long lectures; 32 pages; 29 figures; based on material from arXiv:1709.06195 and arXiv:2008.09673