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High Energy Physics - Phenomenology
Title: The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows
(Submitted on 18 Sep 2023 (v1), last revised 16 May 2024 (this version, v3))
Abstract: We propose the NFLikelihood, an unsupervised version, based on Normalizing Flows, of the DNNLikelihood proposed in Ref.[1]. We show, through realistic examples, how Autoregressive Flows, based on affine and rational quadratic spline bijectors, are able to learn complicated high-dimensional Likelihoods arising in High Energy Physics (HEP) analyses. We focus on a toy LHC analysis example already considered in the literature and on two Effective Field Theory fits of flavor and electroweak observables, whose samples have been obtained throught the HEPFit code. We discuss advantages and disadvantages of the unsupervised approach with respect to the supervised one and discuss possible interplays of the two.
Submission history
From: Humberto Reyes-González [view email][v1] Mon, 18 Sep 2023 13:13:47 GMT (15763kb,D)
[v2] Tue, 9 Apr 2024 13:14:45 GMT (20408kb,D)
[v3] Thu, 16 May 2024 15:05:14 GMT (18030kb,D)
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