Self-supervised anomaly detection for new physics

• Verfasst von: Dillon, Barry M. [VerfasserIn]
• Verfasst von: Mastandrea, Radha [VerfasserIn]
• Verfasst von: Nachman, Benjamin [VerfasserIn]
• Titel: Self-supervised anomaly detection for new physics
• Verf.angabe: Barry M. Dillon, Radha Mastandrea, and Benjamin Nachman
• E-Jahr: 2022
• Jahr: 8 September 2022
• Umfang: 12 S.
• Fussnoten: Gesehen am 08.02.2023
• Titel Quelle: Enthalten in: Physical review
• Ort Quelle: Ridge, NY : American Physical Society, 2016
• Jahr Quelle: 2022
• Band/Heft Quelle: 106(2022), 5, Artikel-ID 056005, Seite 1-12
• ISSN Quelle: 2470-0029
• DOI: doi:10.1103/PhysRevD.106.056005
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 183370584X

Abstract

We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural network to encode simulated QCD “event space” dijets into a low-dimensional “latent space” representation. We optimize the network using the self-supervised contrastive loss, which encourages the preservation of known physical symmetries of the dijets. We then train a binary classifier to discriminate a beyond the standard model resonant dijet signal from a QCD dijet background both in the event space and the latent space representations. We find the classifier performances on the event and latent spaces to be comparable. We finally perform an anomaly detection search using a weakly supervised bump hunt on the latent space dijets, finding again a comparable performance to a search run on the physical space dijets. This opens the door to using low-dimensional latent representations as a computationally efficient space for resonant anomaly detection in generic particle collision events.