  We leverage recent breakthroughs in neural density estimation to propose a
new unsupervised anomaly detection technique (ANODE). By estimating the
probability density of the data in a signal region and in sidebands, and
interpolating the latter into the signal region, a likelihood ratio of data vs.
background can be constructed. This likelihood ratio is broadly sensitive to
overdensities in the data that could be due to localized anomalies. In
addition, a unique potential benefit of the ANODE method is that the background
can be directly estimated using the learned densities. Finally, ANODE is robust
against systematic differences between signal region and sidebands, giving it
broader applicability than other methods. We demonstrate the power of this new
approach using the LHC Olympics 2020 R\&D Dataset. We show how ANODE can
enhance the significance of a dijet bump hunt by up to a factor of 7 with a
10\% accuracy on the background prediction. While the LHC is used as the
recurring example, the methods developed here have a much broader applicability
to anomaly detection in physics and beyond.
