  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.
