Anomaly detection from mass unspecific jet tagging
arXiv:2111.02647 · doi:10.1140/epjc/s10052-022-10058-w
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.
arXiv commentLaTeX 21 pages, 55 plots. Added more comparisons with other methods. Final version in EPJC
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- Neural Embedding: Learning the Embedding of the Manifold of Physics Data
- A method for approximating optimal statistical significances with machine-learned likelihoods
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