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The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows
April 10, 2024, 4:43 a.m. | Humberto Reyes-Gonzalez, Riccardo Torre
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv cs.lg energy examples hep-ex hep-ph learn physics show spline through type unsupervised
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