BibTeX

@article{2603.15322v1,
Author        = {Julia P. Woodward and Joshua Villarreal and John M. Hardin and Austin Schneider and Janet M. Conrad},
Title         = {A Simulation-Based Inference Evaluation of Tension Between MicroBooNE and MiniBooNE Results in a 3+1 Sterile Neutrino Global Fit},
Eprint        = {2603.15322v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ex},
Abstract      = {Compatibility between different datasets in a global fit is essential for determining whether a chosen model adequately describes the data. In a 3+1 sterile neutrino global fit, long-standing tensions between datasets sensitive to $ν_e$ appearance and $ν_e/ν_μ$ disappearance indicate a failure of the model to explain the observed data, despite an overall $> 5σ$ improvement over the $3ν$ Standard Model (SM) based on a $χ^2$ fit. Overall, a global preference for the 3+1 sterile-neutrino hypothesis with significant tension between experiments motivates consideration of more complex models, but these are currently computationally prohibitive to evaluate. This paper is the third in a series aimed at reducing computational cost by developing a Simulation-Based Inference (SBI) framework for global fits. Previous papers focused on rapidly fitting the data sets using frequentist (Feldman-Cousins) and Bayesian approaches, while in this work, we formalize a definition of tension within the SBI framework.
  As an example, we perform a full 3+1 fit to the charged-current quasi-elastic neutrino data from the MiniBooNE experiment and the inclusive neutrino data from the MicroBooNE experiment, located on the same beamline. Using experiment-supplied systematics as is, we find these data sets favor 3+1 at $3.6σ$ and $1.8σ$ respectively, while the tension between the two is $3.3σ$, when fit with the SBI procedure. After correcting for normalization differences between data and Monte Carlo in the MicroBooNE $ν_μ$ samples, the tension relaxes to $2.2σ$, indicating reduced but non-negligible disagreement. The observed tension may reflect both limitations of the 3+1 model in describing the datasets and the presence of systematic effects that impact the experiments differently.},
Year          = {2026},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2603.15322v1},
File          = {2603.15322v1.pdf}
}