More QCD Masterclass Lectures on Jet Physics and Machine Learning
Abstract
These lectures were presented at the 2026 QCD Masterclass in Saint-Jacut-de-la-mer, France. They supplement the published notes from the 2024 school with presentation of a few new topics that have become popular in recent years. These include event metrics, anomaly detection, scaling laws, the limits of classification performance, and regression that respects spacetime symmetries. Many of the presented results are known, but some new results are derived, especially related to anomaly detection. As was the case in those original notes, while all these problems are motivated from machine learning applied to QCD, no discussion of details of machine learning is presented in these notes. All results follow from application of fundamental principles of QCD. I begin these notes with an apology for humanist physics.
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BibTeX
@misc{larkoski2026more,
title = {{More QCD Masterclass Lectures on Jet Physics and Machine Learning}},
author = {Andrew J. Larkoski},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.09785},
eprint = {2609.09785},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.09785},
}
RIS
TY - UNPB
TI - More QCD Masterclass Lectures on Jet Physics and Machine Learning
AU - Andrew J. Larkoski
PY - 2026
DA - 2026/10/24
PB - arXiv
DO - 10.48550/arxiv.2609.09785
UR - https://arxiv.org/abs/2609.09785
AB - These lectures were presented at the 2026 QCD Masterclass in Saint-Jacut-de-la-mer, France. They supplement the published notes from the 2024 school with presentation of a few new topics that have become popular in recent years. These include event metrics, anomaly detection, scaling laws, the limits of classification performance, and regression that respects spacetime symmetries. Many of the presented results are known, but some new results are derived, especially related to anomaly detection. As was the case in those original notes, while all these problems are motivated from machine learning applied to QCD, no discussion of details of machine learning is presented in these notes. All results follow from application of fundamental principles of QCD. I begin these notes with an apology for humanist physics.
ER -