AI 中文总结
本研究通过基于模拟推断的联合拟合，评估MiniBooNE与MicroBooNE数据在3+1惰性中微子模型下的张力，发现MicroBooNE降低了MiniBooNE的偏好显著性，且两实验间存在≥2.5σ的张力。
AI 中文摘要
MiniBooNE低能超出及其随后被MicroBooNE排除，为短基线中微子反常的惰性中微子解释提供了重要检验。由于两个实验在分析中采用不同方法，这一检验变得复杂。在本贡献中，我们提出一种对两个实验一致的处理方法。我们对MiniBooNE和MicroBooNE数据在$3+1$惰性中微子模型下进行联合拟合，省略所有数据驱动因素，使用来自助推器中微子束（BNB）和主注入器中微子（NuMI）束线的MicroBooNE数据，并评估两个实验结果之间的参数拟合优度（PG）张力。对参数拟合和PG张力进行严格的频率论处理具有挑战性。现有方法需要渐近假设，已知这些假设对中微子振荡测量或重复似然优化任务不准确。为此，我们使用先前开发的基于模拟推断（SBI）的频率论拟合框架，并引入一种新的基于SBI的PG张力评估方法，使所需的基于试验的校准在计算上可行。我们发现，MicroBooNE的加入降低了MiniBooNE对惰性中微子振荡偏好的显著性，基于试验的3+1偏好仍为$2.7σ$。两个实验在$3+1$模型内表现出$\u22652.5σ$的PG张力。
英文摘要
The MiniBooNE low-energy excess and its subsequent exclusion by MicroBooNE provide an important test of sterile-neutrino explanations of short-baseline neutrino anomalies. Such a test is complicated by the fact that both experiments use different approaches to their analyses. In this contribution, we present a consistent approach to both experiments. We perform a joint fit of MiniBooNE and MicroBooNE data to a $3+1$ sterile-neutrino model omitting all data-driven factors, using MicroBooNE data from both the Booster Neutrino Beam (BNB) and Neutrinos at the Main Injector (NuMI) beamlines, and evaluate the parameter goodness-of-fit (PG) tension between the two experimental results. A rigorous frequentist treatment of both parameter fitting and PG tension is challenging. Existing methods require asymptotic assumptions known to be inaccurate for neutrino oscillation measurements or repeated likelihood optimization tasks. To this end, we use a previously developed frequentist fitting framework based on simulation-based inference (SBI) and introduce a new SBI-based method for evaluating PG tension that makes the required trial-based calibration computationally feasible. We find that the addition of MicroBooNE reduces the significance of the MiniBooNE preference for sterile-neutrino oscillations, with a trials-based preference for 3+1 remaining at $2.7 σ$. The two experiments exhibit a $\geq 2.5σ$ PG tension within the $3+1$ model.
CommentsSubstantially expanded and revised article-version of conference whitepaper arxiv:2603.15322
