Anomalies, Representations, and Self-Supervision
- URL: http://arxiv.org/abs/2301.04660v1
- Date: Wed, 11 Jan 2023 19:00:00 GMT
- Title: Anomalies, Representations, and Self-Supervision
- Authors: Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman
Plehn
- Abstract summary: We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC 2021.
The AnomalyCLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We develop a self-supervised method for density-based anomaly detection using
contrastive learning, and test it using event-level anomaly data from CMS
ADC2021. The AnomalyCLR technique is data-driven and uses augmentations of the
background data to mimic non-Standard-Model events in a model-agnostic way. It
uses a permutation-invariant Transformer Encoder architecture to map the
objects measured in a collider event to the representation space, where the
data augmentations define a representation space which is sensitive to
potential anomalous features. An AutoEncoder trained on background
representations then computes anomaly scores for a variety of signals in the
representation space. With AnomalyCLR we find significant improvements on
performance metrics for all signals when compared to the raw data baseline.
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