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.
Cited by 2
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 University of Manchester
- 2 Universitat de València / University of Valencia [UV]
- 3 University of Sussex
- European Commission [EC]
- Generalitat Valenciana
- Ministerio de Educación y Cultura - Spain (MEC) (through Organization: Ministerio de Educación y Cultura - España / Ministry of Education and Culture - Spain [MEC Spain])
- Royal Society
- Science and Technology Facilities Council [STFC]