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.
Cite
@article{arxiv.2301.04660,
title = {Anomalies, Representations, and Self-Supervision},
author = {Barry M. Dillon and Luigi Favaro and Friedrich Feiden and Tanmoy Modak and Tilman Plehn},
journal= {arXiv preprint arXiv:2301.04660},
year = {2024}
}