  The nonlinear energy response of cryogenic microcalorimeters is usually
corrected through an empirical calibration. X-ray or gamma-ray emission lines
of known shape and energy anchor a smooth function that generalizes the
calibration data and converts detector measurements to energies. We argue that
this function should be an approximating spline. The theory of Gaussian process
regression makes a case for this functional form. It also provides an important
benefit previously absent from our calibration method: a quantitative
uncertainty estimate for the calibrated energies, with lower uncertainty near
the best-constrained calibration points.
