  We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a
method for generating signal templates in resonant anomaly detection searches.
RAD-OT leverages the fact that the conditional probability density of the
target features vary approximately linearly along the optimal transport path
connecting the resonant feature. This does not assume that the conditional
density itself is linear with the resonant feature, allowing RAD-OT to
efficiently capture multimodal relationships, changes in resolution, etc. By
solving the optimal transport problem, RAD-OT can quickly build a template by
interpolating between the background distributions in two sideband regions. We
demonstrate the performance of RAD-OT using the LHC Olympics R\&D dataset,
where we find comparable sensitivity and improved stability with respect to
deep learning-based approaches.
