Unsupervised in-distribution anomaly detection of new physics through conditional density estimation

Abstract

Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data. Here we instead present and motivate a method for unsupervised in-distribution anomaly detection using a conditional density estimator, designed to find unique, yet completely unknown, sets of samples residing in high probability density regions. We apply this method towards the detection of new physics in simulated Large Hadron Collider (LHC) particle collisions as part of the 2020 LHC Olympics blind challenge, and show how we detected a new particle appearing in only 0.08% of 1 million collision events. The results we present are our original blind submission to the 2020 LHC Olympics, where it achieved the state-of-the-art performance.

arXiv commentAccepted to NeurIPS Machine Learning and the Physical Sciences workshop. See arXiv:2007.00674 for further methods

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• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
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• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions