Anomaly detection from mass unspecific jet tagging

Author:

Aguilar-Saavedra J. A.

Abstract

AbstractWe introduce a novel anomaly search method based on (i) jet tagging to select interesting events, which are less likely to be produced by background processes; (ii) comparison of the untagged and tagged samples to single out features (such as bumps produced by the decay of new particles) in the latter. We demonstrate the usefulness of this method by applying it to a final state with two massive boosted jets: for the new physics benchmarks considered, the signal significance increases an order of magnitude, up to a factor of 40. We compare to other anomaly detection methods in the literature and discuss possible generalisations.

Funder

Fundação para a Ciência e a Tecnologia

Ministerio de Ciencia e Innovación

Publisher

Springer Science and Business Media LLC

Subject

Physics and Astronomy (miscellaneous),Engineering (miscellaneous)

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine-learned exclusion limits without binning;The European Physical Journal C;2023-12-19

2. Neural embedding: learning the embedding of the manifold of physics data;Journal of High Energy Physics;2023-07-12

3. Simulation-based anomaly detection for multileptons at the LHC;Journal of High Energy Physics;2023-01-13

4. Anomaly detection under coordinate transformations;Physical Review D;2023-01-09

5. Multiboson signals in the UN2HDM;The European Physical Journal C;2022-11-30

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