Anomaly detection with Convolutional Graph Neural Networks

TL;DR

This paper introduces a novel autoencoder approach using Graph Neural Networks for anomaly detection in particle physics, capable of distinguishing new physics signals from standard model backgrounds.

Contribution

It presents a symmetric GNN autoencoder architecture that reconstructs both node and edge features, enhancing anomaly detection capabilities in boosted jet analysis.

Findings

Effective in identifying W bosons, top quarks, and exotic scalars

Improves separation of new physics signals from QCD background

Demonstrates broad applicability across different particle types

Abstract

We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based discriminators, we find that such setups provide a promising avenue to isolate new physics and competing SM signatures from sensitivity-limiting QCD jet contributions. We demonstrate the flexibility and broad applicability of this approach using examples of $W$ bosons, top quarks, and exotic hadronically-decaying exotic scalar bosons.