Deep Set Auto Encoders for Anomaly Detection in Particle Physics

TL;DR

This paper introduces a Deep Set Variational Autoencoder for anomaly detection in particle physics, demonstrating superior performance in the Dark Machines Challenge by focusing on latent space representations without decoding.

Contribution

The paper presents a novel Deep Set Variational Autoencoder architecture that enhances anomaly detection in particle physics data, especially when decoding is omitted.

Findings

Achieves top anomaly detection performance in Dark Machines Challenge

Best results obtained when using latent space representation alone

Outperforms other models on both open and blinded datasets

Abstract

There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider. We introduce a Deep Set Variational Autoencoder and present results on the Dark Machines Anomaly Score Challenge. We find that the method attains the best anomaly detection ability when there is no decoding step for the network, and the anomaly score is based solely on the representation within the encoded latent space. This method was one of the top-performing models in the Dark Machines Challenge, both for the open data sets as well as the blinded data sets.