Testable Likelihoods for Beyond-the-Standard Model Fits
Anja Beck Méril Reboud Danny van Dyk
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.