Online-compatible Unsupervised Non-resonant Anomaly Detection

TL;DR

This paper introduces a novel online-compatible unsupervised anomaly detection method using decorrelated autoencoders, enabling effective non-resonant anomaly detection with background estimation, suitable for particle physics searches.

Contribution

It presents the first complete online-compatible unsupervised anomaly detection strategy combining signal sensitivity and data-driven background estimation.

Findings

Achieves excellent performance on ADC2021 data challenge signals.

First method to enable online non-resonant anomaly detection.

Uses decorrelated autoencoders for robust background estimation.

Abstract

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events - there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection of non-resonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously-trained autoencoders that are forced to be decorrelated from each other. This method can be deployed offline for non-resonant anomaly detection and is also the first complete online-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.