Testable Likelihoods for Beyond-the-Standard Model Fits
This work addresses a domain-specific challenge in particle physics for researchers needing precise BSM model fits, representing an incremental improvement by applying existing methods to a new context.
The authors tackled the problem of accurately transferring information from low-energy measurements to high-energy Beyond-the-Standard Model fits by proposing the use of normalizing flows to construct likelihood functions, which enabled sample generation and a goodness-of-fit test, achieving quantified accuracy in a multi-modal and non-Gaussian example.
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 $χ^2$ 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.