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Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
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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.
Forward citations
Cited by 3 Pith papers
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Multi-scale Optimal Transport for Complete Collider Events
A hierarchical optimal transport distance, built by measuring events as distributions of jets whose shapes are measured by optimal transport, improves classification of simulated LHC events.
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Graph theory inspired anomaly detection at the LHC
Sparse globally rigid graph representations of jets, combined with roughly 30 reclustered subjets, improve graph autoencoder anomaly detection on the LHC Olympics benchmark.
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Optimal Transport Event Representation for Anomaly Detection
Adding a few optimal-transport-based features to standard jet observables nearly doubles anomaly-detection significance at 0.5% signal injection on LHC Olympics benchmarks.
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