A two-stage simulation-based inference method, using dropout neural networks and normalizing flows, produces fast approximate credibility regions for 3+1 sterile neutrino global fits.
II A), use sblmc to simulate experimental data from said model parameters, and then use fcmlc to build an estimate of the posterior distribution from which CRs can be drawn
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Feldman-Cousins' ML Cousin: Sterile Neutrino Global Fits using Simulation-Based Inference
A two-stage simulation-based inference method, using dropout neural networks and normalizing flows, produces fast approximate credibility regions for 3+1 sterile neutrino global fits.