Work

Anomaly Awareness

Year: 2020

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2007.14462

Authors: Khosa, Charanjit K., Sanz, Veronica

Institutions: University of Sussex, Universitat de València, Instituto de Física Corpuscular

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Generalitat Valenciana, Science and Technology Facilities Council, European Regional Development Fund

Awards: G1888088267, G1892091120, G1953887145, G2538099035, G2898646471, G4182428297, G4322134188, G4364689995, G4378405153, G556388351, G6355921156, G6535118496, G6717400142, Theoretical Particle Physics Consortium Sussex - Royal Holloway

OpenAlex ID: W4303454884

Cites:

Cited by: 13

Related to: 10