The Mass-ive Issue: Anomaly Detection in Jet Physics
High Energy Physics - Phenomenology
2023-03-27 v1
Abstract
In the hunt for new and unobserved phenomena in particle physics, attention has turned in recent years to using advanced machine learning techniques for model independent searches. In this paper we highlight the main challenge of applying anomaly detection to jet physics, where preserving an unbiased estimator of the jet mass remains a critical piece of any model independent search. Using Variational Autoencoders and multiple industry-standard anomaly detection metrics, we demonstrate the unavoidable nature of this problem.
Keywords
Cite
@article{arxiv.2303.14134,
title = {The Mass-ive Issue: Anomaly Detection in Jet Physics},
author = {Tobias Golling and Takuya Nobe and Dimitrios Proios and John Andrew Raine and Debajyoti Sengupta and Slava Voloshynovskiy and Jean-Francois Arguin and Julien Leissner Martin and Jacinthe Pilette and Debottam Bakshi Gupta and Amir Farbin},
journal= {arXiv preprint arXiv:2303.14134},
year = {2023}
}
Comments
6 pages, 5 figures. Accepted at Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)