IRC-safe Graph Autoencoder for unsupervised anomaly detection

Understand

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model.

• Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures.
• In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing.
• We demonstrate that whilst this approach has theoretically favourable properties, it also exhibits formidable sensitivity to non-QCD structures.