Studying potential BSM effects at the precision frontier requires accurate transfer of information from low-energy measurements to high-energy BSM models. We propose to use normalising flows to construct likelihood functions that achieve this transfer. Likelihood functions constructed in this way provide the means to generate additional samples and admit a ``trivial'' goodness-of-fit test in form of a $\chi^2$ test statistic. Here, we study a particular form of normalising flow, apply it to a multi-modal and non-Gaussian example, and quantify the accuracy of the likelihood function and its test statistic.

翻译：在精度前沿研究潜在超出标准模型（BSM）效应，需要将低能测量信息准确传递至高能BSM模型。我们提出利用标准化流构建实现这一传递的似然函数。以此方式构建的似然函数不仅能够生成额外样本，还可通过$\chi^2$检验统计量实现"平凡"的拟合优度检验。本研究聚焦特定形式的标准化流，将其应用于多模态非高斯示例，并量化似然函数及其检验统计量的精度。