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.
Raster scan or 2-D approach?
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abstract
We consider the relative merits of two different approaches to discovery or exclusion of new phenomena, a raster scan or a 2-dimensional approach.
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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.