REVIEW 1 cited by
Anomaly Awareness
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
Forward citations
Cited by 1 Pith paper
-
Enhancing anomaly detection with topology-aware autoencoders
Autoencoders with latent spaces shaped like S^2, S^2×S^2, or RP^2, matched to the phase-space topology of the background, reduce spurious reconstruction errors and give a small but consistent anomaly-detection gain ov...
Discussion (0). Continue with ORCID to comment.