  We present R-ANODE, a new method for data-driven, model-agnostic resonant
anomaly detection that raises the bar for both performance and
interpretability. The key to R-ANODE is to enhance the inductive bias of the
anomaly detection task by fitting a normalizing flow directly to the small and
unknown signal component, while holding fixed a background model (also a
normalizing flow) learned from sidebands. In doing so, R-ANODE is able to
outperform all classifier-based, weakly-supervised approaches, as well as the
previous ANODE method which fit a density estimator to all of the data in the
signal region instead of just the signal. We show that the method works equally
well whether the unknown signal fraction is learned or fixed, and is even
robust to signal fraction misspecification. Finally, with the learned signal
model we can sample and gain qualitative insights into the underlying anomaly,
which greatly enhances the interpretability of resonant anomaly detection and
offers the possibility of simultaneously discovering and characterizing the new
physics that could be hiding in the data.
