AI 中文总结
研究高精度长基线中微子实验利用近探测器数据约束系统不确定性问题，采用归一化流结合高斯近似训练模型，为该问题提供可靠似然模型，效果优于高斯近似。
AI 中文摘要
精确的长基线中微子实验使用近探测器数据来约束未振荡中微子通量的系统不确定性，这是在远探测器进行精确振荡参数测量的前提。当约束似然性是高维且非高斯时，此过程需要先进的统计处理。我们表明归一化流为该问题提供了忠实且便携的似然模型。利用似然性的初始高斯近似，我们训练了一种结合耦合变换和自回归样条流的混合架构。我们在具有110个系统不确定性参数的代表性近探测器似然性复制品上演示了该方法，其中10个在后部明确引入了非高斯性。训练后的模型实现了98%的相对有效样本大小，而高斯近似约为5%，并且再现了马尔可夫链蒙特卡罗参考，同时保持封闭形式、可采样和逐点可评估，使其适用于下游不确定性传播以及未来从近探测器到远探测器的拟合。
英文摘要
Precision long-baseline neutrino experiments use near-detector data to constrain systematic uncertainties on the unoscillated neutrino flux, a prerequisite for accurate oscillation parameter measurements at the far detector. When the constrained likelihood is high-dimensional and non-Gaussian, this procedure demands advanced statistical treatment. Here we show that normalizing flows provide faithful and portable likelihood models for this problem. Leveraging an initial Gaussian approximation of the likelihood, we train a hybrid architecture combining coupling transformations and autoregressive spline flows. We demonstrate the method on a representative near-detector likelihood replica with 110 systematic uncertainty parameters, 10 of which explicitly introduce non-Gaussianities in the posterior. The trained model achieves a relative effective sample size of 98%, compared with about 5% for the Gaussian approximation, and reproduces a Markov chain Monte Carlo reference while remaining closed-form, samplable, and pointwise evaluable, making it suited to downstream uncertainty propagation and future near-detector to far-detector fits.
CommentsVersion 2. Improved methods section and refined typos and phrasing details in main text
