Energy calibration of nonlinear microcalorimeters with uncertainty estimates from Gaussian process regression
Abstract
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.
Keywords
Cite
@article{arxiv.2204.08431,
title = {Energy calibration of nonlinear microcalorimeters with uncertainty estimates from Gaussian process regression},
author = {J. W. Fowler and B. K. Alpert and G. C. O'Neil and D. S. Swetz and J. N. Ullom},
journal= {arXiv preprint arXiv:2204.08431},
year = {2022}
}
Comments
Submitted to J. Low Temperature Physics for the Proceedings of the 19th International Workshop on Low-Temperature Detectors (2021)