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

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