BibTeX

@article{2507.01153v2,
Author        = {Joshua Villarreal and Julia Woodward and John Hardin and Janet Conrad},
Title         = {A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino
  Global Fits},
Eprint        = {2507.01153v2},
DOI           = {10.1088/2632-2153/ae040c},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
Abstract      = {A critical challenge in particle physics is combining results from diverse
experimental setups that measure the same physical quantity to enhance
precision and statistical power, a process known as a global fit. Global fits
of sterile neutrino searches, hunts for additional neutrino oscillation
frequencies and amplitudes, present an intriguing case study. In such a
scenario, the key assumptions underlying Wilks' theorem, a cornerstone of most
classic frequentist analyses, do not hold. The method of Feldman and Cousins, a
trials-based approach which does not assume Wilks' theorem, becomes
computationally prohibitive for complex or intractable likelihoods. To bypass
this limitation, we borrow a technique from simulation-based inference (SBI) to
estimate likelihood ratios for use in building trials-based confidence
intervals, speeding up test statistic evaluations by a factor $>10^4$ per grid
point, resulting in a faster, but approximate, frequentist fitting framework.
Applied to a subset of sterile neutrino search data involving the disappearance
of muon-flavor (anti)neutrinos, our method leverages machine learning to
compute frequentist confidence intervals while significantly reducing
computational expense. In addition, the SBI-based approach holds additional
value by recognizing underlying systematic uncertainties that the Wilks
approach does not. Thus, our method allows for more robust machine
learning-based analyses critical to performing accurate but computationally
feasible global fits. This allows, for the first time, a global fit to sterile
neutrino data without assuming Wilks' theorem. While we demonstrate the utility
of such a technique studying sterile neutrino searches, it is applicable to
both single-experiment and global fits of all kinds.},
Year          = {2025},
Month         = {Jul},
Note          = {Mach. Learn.: Sci. Technol. 6 035053 (2025)},
Url           = {http://arxiv.org/abs/2507.01153v2},
File          = {2507.01153v2.pdf}
}