UC Berkeley

We leverage recent breakthroughs in neural density estimation to propose a new unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By estimating the conditional probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a fully data-driven likelihood ratio of data versus 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.