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arXiv · 2502.09494

Communicating Likelihoods with Normalising Flows

Jack Y. Araz·Anja Beck·Méril Reboud·Michael Spannowsky·Danny van Dyk
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Abstract

We present a machine-learning-based workflow to model an unbinned likelihood from its samples. A key advancement over existing approaches is the validation of the learned likelihood using rigorous statistical tests of the joint distribution, such as the Kolmogorov-Smirnov test of the joint distribution. Our method enables the reliable communication of experimental and phenomenological likelihoods for subsequent analyses. We demonstrate its effectiveness through three case studies in high-energy physics. To support broader adoption, we provide an open-source reference implementation, nabu.

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Jack Y. Araz, Anja Beck, Méril Reboud, Michael Spannowsky, Danny van Dyk. 2025-02-13. Communicating Likelihoods with Normalising Flows. https://doi.org/10.1140/epjc%2Fs10052-026-16045-9

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