论文标题
使用神经样条流动的实验性中微子振荡的可能性推断
Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows
论文作者
论文摘要
在机器学习中，无似然推理是指执行由数据驱动的分析而不是分析表达式的任务。我们讨论了神经样条流（神经密度估计算法）在长基线中微子实验中中微子振荡参数测量的可能性推理问题中的应用。开发了一种适合物理参数推断的方法，并将其应用于T2K实验中消失的MUON中微子分析的情况。
In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the likelihood-free inference problem of the measurement of neutrino oscillation parameters in Long Baseline neutrino experiments. A method adapted to physics parameter inference is developed and applied to the case of the disappearance muon neutrino analysis at the T2K experiment.
