Work

Anomaly detection from mass unspecific jet tagging

Year: 2022
Type: article
Abstract: We 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.
Language: English
Open Access status: diamond
OpenAlex ID: W3210072687
FWCI: 0.628
Cites: 48
Cited by: 21
Related to: 20