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Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models

Alvarez, E.; Spannowsky, M.; Szewc, M.

Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models Thumbnail


E. Alvarez

M. Szewc


The classification of jets induced by quarks or gluons is important for New Physics searches at high-energy colliders. However, available taggers usually rely on modeling the data through Monte Carlo simulations, which could veil intractable theoretical and systematical uncertainties. To significantly reduce biases, we propose an unsupervised learning algorithm that, given a sample of jets, can learn the SoftDrop Poissonian rates for quark- and gluon-initiated jets and their fractions. We extract the Maximum Likelihood Estimates for the mixture parameters and the posterior probability over them. We then construct a quark-gluon tagger and estimate its accuracy in actual data to be in the 0.65–0.7 range, below supervised algorithms but nevertheless competitive. We also show how relevant unsupervised metrics perform well, allowing for an unsupervised hyperparameter selection. Further, we find that this result is not affected by an angular smearing introduced to simulate detector effects for central jets. The presented unsupervised learning algorithm is simple; its result is interpretable and depends on very few assumptions.


Alvarez, E., Spannowsky, M., & Szewc, M. (2022). Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models. Frontiers in Artificial Intelligence, 5, Article 852970.

Journal Article Type Article
Acceptance Date Feb 18, 2022
Online Publication Date Mar 17, 2022
Publication Date 2022
Deposit Date Jul 6, 2022
Publicly Available Date Jul 6, 2022
Journal Frontiers in Artificial Intelligence
Print ISSN 2624-8212
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 5
Article Number 852970


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Copyright Statement
© 2022 Alvarez, Spannowsky and Szewc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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