Anomaly Detection with Density Estimation
This addresses anomaly detection in physics and beyond, offering a robust method with broad applicability, though it builds incrementally on existing density estimation techniques.
The paper tackled unsupervised anomaly detection by proposing ANODE, a method that uses neural density estimation to construct likelihood ratios for identifying localized anomalies, achieving up to a 7-fold enhancement in significance for a dijet bump hunt with 10% background prediction accuracy.
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.