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arXiv · 2608.19317

NAE, Statistically

Ranit Das·Jonathan Ostertag-Henning·Tilman Plehn·Lorenz Vogel
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Abstract

Searches for new physics using neural anomaly scores have transformative potential, but suffer from a lack of statistical interpretability. The normalized autoencoder (NAE) provides a probabilistic interpretation of the standard bottleneck architecture, tying the anomaly score to a learned likelihood. We validate this relation for a toy model, test it for jets using a dual-NAE setup, and show how a Bayesian NAE learns this likelihood with an uncertainty.

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Ranit Das, Jonathan Ostertag-Henning, Tilman Plehn, Lorenz Vogel. 2026-08-19. NAE, Statistically. https://arxiv.org/abs/2608.19317

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