TY - JOUR
AU - Torre, Riccardo
AB - 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.
TI - The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows
JF - High Energy Physics - Experiment
DO - 10.48550/arxiv.2309.09743
DA - 2023-09-18
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/the-nflikelihood-an-unsupervised-dnnlikelihood-from-normalizing-flows-jg2Y2Acrnr
VL - 2024
IS - 2309
DP - DeepDyve
ER -