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IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection
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DOI：10.3389/frai.2022.943135.png)
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Abstract
En 中文
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 favorable properties, it also exhibits formidable sensitivity to non-QCD structures.
Keywords:
anomaly detection
graph neural network
high energy physics
IRC safety
anomalous jets
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