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A covariant energy density functional is calibrated using a principledBayesian statistical framework informed by experimental binding energies andcharge radii of several magic and semi-magic nuclei. The Bayesian samplingrequired for the calibration is enabled by the emulation of the high-fidelitymodel through the implementation of a reduced basis method (RBM) - a set ofdimensionality reduction techniques that can speed up demanding calculationsinvolving partial differential equations by several orders of magnitude. TheRBM emulator we build - using only 100 evaluations of the high-fidelity model -is able to accurately reproduce the model calculations in tens of millisecondson a personal computer, an increase in speed of nearly a factor of 3,300 whencompared to the original solver. Besides the analysis of the posteriordistribution of parameters, we present predictions with properly estimateduncertainties for observables not included in the fit, specifically the neutronskin thickness of 208Pb and 48Ca, as reported by PREX and CREX collaborations.The straightforward implementation and outstanding performance of the RBM makesit an ideal tool for assisting the nuclear theory community in providingreliable estimates with properly quantified uncertainties of physicalobservables. Such uncertainty quantification tools will become essential giventhe expected abundance of data from the recently inaugurated and futureexperimental and observational facilities.

Bayes goes fast: Uncertainty Quantification for a Covariant Energy Density Functional emulated by the Reduced Basis Method