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.
Net- works used for the estimation of the underlying oscilla- tion parameters were created and trained using Python’s tensorflow API (Ref
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
hep-ex 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.