Bayesian calibration and truncation error estimation of chiral effective theories

In the era of increasingly precise nuclear experiments, it has become imperative that high-quality models for nuclear interactions are created. However, we must extend our lens of high-quality to include rigorous quantification of uncertainty to compare model predictions to experimental observations properly. To this end, we employ models derived from Effective Field Theories (EFTs) derived from quantum chromodynamics, in particular pionless and chiral EFTs, as these have been highly useful in accurately calculating nuclear observables. Not only that, the perturbative structure of such EFTs provides a framework for an improbable interaction while simultaneously offering a robust and straightforward means of estimating model uncertainties by the truncation of the EFT model. Considering these ideas, we can employ a Bayesian framework to calibrate EFT models and estimate the truncation error. Furthermore, we identify the need to employ machine-learning techniques to emulate the calculation of nuclear observables to increase the deployability of Bayesian methods by reducing computational expenses.