  Nuclear density functional theory is the prevalent theoretical framework for
accurately describing nuclear properties at the scale of the entire chart of
nuclides. Given an energy functional and a many-body scheme (e.g., single- or
multireference level), the predictive power of the theory depends strongly on
how the parameters of the energy functionals have been calibrated with
experimental data. Expanded algorithms and computing power have enabled recent
optimization protocols to include data in deformed nuclei in order to optimize
the coupling constants of the energy functional. The primary motivation of this
work is to test the robustness of such protocols with respect to some of the
technical and numerical details of the underlying calculations, especially when
the calibration explores a large parameter space. To this end, we quantify the
effect of these uncertainties on both the optimization and statistical
emulation of composite objective functions. We also emphasize that Bayesian
calibration can provide better estimates of the theoretical errors used to
define objective functions.
