arXiv · 2309.09743
The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows
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.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Humberto Reyes-Gonzalez, Riccardo Torre. 2023-09-18. The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows. https://arxiv.org/abs/2309.09743
Cite the original work for its findings. Save a collection to share your selection of sources.