BibTeX

@article{2501.08988v1,
Author        = {Joshua Villarreal and John M. Hardin and Janet M. Conrad},
Title         = {Feldman-Cousins' ML Cousin: Sterile Neutrino Global Fits using
  Simulation-Based Inference},
Eprint        = {2501.08988v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ex},
Abstract      = {For many small-signal particle physics analyses, Wilks' theorem, a
simplifying assumption that presumes log-likelihood asymptotic normality, does
not hold. The most common alternative approach applied in particle physics is a
highly computationally expensive procedure put forward by Feldman and Cousins.
When many experiments are combined for a global fit to data, deviations from
Wilks' theorem are exacerbated, and Feldman-Cousins becomes computationally
intractable. We present a novel, machine learning-based procedure that can
approximate a full-fledged Bayesian analysis 200 times faster than the
Feldman-Cousins method. We demonstrate the utility of this novel method by
performing a joint analysis of electron neutrino/antineutrino disappearance
data within a single sterile neutrino oscillation framework. Although we
present a prototypical simulation-based inference method for a sterile neutrino
global fit, we anticipate that similar procedures will be useful for global
fits of all kinds, especially those in which Feldman-Cousins is too
computationally expensive to use.},
Year          = {2025},
Month         = {Jan},
Url           = {http://arxiv.org/abs/2501.08988v1},
File          = {2501.08988v1.pdf}
}