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Workshop: Machine Learning and the Physical Sciences

One-Class Dense Networks for Anomaly Detection

Norman Karr · Benjamin Nachman · David Shih


Unsupervised learning has been proposed as a tool for model agnostic anomaly detection (AD) in collider physics. While the goal of these approaches is usually to find events that are `rare' under the Standard Model hypothesis, many approaches are governed by heuristics to strive towards an implicit density estimator. We study the simplest possible one-class classification method for unsupervised AD and show that it has similar properties to other unsupervised methods. The method is illustrated using a Gaussian dataset and a simulation of the events at the Large Hadron Collider (LHC). The simplicity of the one-class classification may enable a deeper understanding of unsupervised AD in the future.

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