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Communicating Likelihoods with Normalising Flows

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arxiv 2502.09494 v1 pith:F4DR4LZY submitted 2025-02-13 hep-ph cs.LGhep-exphysics.data-an

Jack Y. Araz , Anja Beck , Méril Reboud , Michael Spannowsky , Danny van Dyk This is my paper
classification hep-phcs.LGhep-exphysics.data-an
keywords distributionjointlikelihoodlikelihoodsadoptionadvancementanalysesapproaches
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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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