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  • Open Access

Residual anomaly detection with density estimation

Ranit Das1,*, Gregor Kasieczka2,†, and David Shih1,‡

  • *Contact author: ranit@physics.rutgers.edu
  • †Contact author: gregor.kasieczka@uni-hamburg.de
  • ‡Contact author: shih@physics.rutgers.edu
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Phys. Rev. D 112, 114049 – Published 31 December, 2025

DOI: https://doi.org/10.1103/6hgk-q52g

Abstract

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.

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Outline

Information

Phys. Rev. D 112, 114049– Published 31 December, 2025
  • Received 12 March 2025
  • Accepted 18 November 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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