Bayesian uncertainty quantification in effective field theories

Effective field theories (EFTs) offer a rigorous connection of quantum chromodynamics to the low energy regime of nuclear structure and interactions. High-quality EFT interactions provide precise, accurate results when paired with modern many-body methods, such as quantum Monte Carlo. However, despite the advancements made with techniques and interactions, there is still a lack of rigorous uncertainty quantification in many theoretical calculations. One way to remedy this is by introducing Bayesian methods in EFT parameter estimation, which can be accomplished by implementing Markov Chain Monte Carlo (MCMC) to sample the EFT parameter posterior. This route, however, generates its difficulties by requiring significant computational resources. To counteract the computational cost, we explore the use of emulation to speed up objective function evaluations in the MCMC algorithm.