Anomalies, Representations, and Self-Supervision

TL;DR

This paper introduces AnomalyCLR, a self-supervised contrastive learning method using a Transformer Encoder for density-based anomaly detection in collider event data, showing significant performance improvements over baseline methods.

Contribution

The paper presents a novel self-supervised anomaly detection approach combining contrastive learning, data augmentation, and Transformer encoders for collider physics data.

Findings

Significant performance improvements over baseline methods.

Effective detection of non-Standard-Model events.

Model-agnostic and data-driven anomaly detection.

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.