Estimating parameter uncertainty in binding-energy models by the frequency-domain bootstrap

Abstract: We propose using the frequency-domain bootstrap (FDB) to estimate errors of modeling parameters when the modeling error is itself a major source of uncertainty. Unlike the usual bootstrap or the simple $\chi^2$ analysis, the FDB can take into account correlations between errors. It is also very fast compared to the the Gaussian process Bayesian estimate as often implemented for computer model calibration. The method is illustrated drop model of nuclear binding energies. We find that the FDB gives a more conservative estimate of the uncertainty in liquid drop parameters in better accord with more empirical estimates. For the nuclear physics application, there no apparent obstacle to apply the method to the more accurate and detailed models based on density-functional theory.