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Energy calibration of nonlinear sensors with uncertainty estimates from Gaussian Process Regression
Published
Author(s)
Joseph Fowler, Bradley Alpert, Galen O'Neil, Daniel Swetz, Joel Ullom
Abstract
The nonlinear energy response of cryogenic x-ray microcalorimeters is usually corrected though an empirical calibration. Certain x-ray emission lines of known shape and calibrated energy are used to anchor a smooth function that gener- alizes the calibration data and translates 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. More importantly, it provides an uncertainty estimate for the calibrated energies.
Fowler, J.
, Alpert, B.
, O'Neil, G.
, Swetz, D.
and Ullom, J.
(2022),
Energy calibration of nonlinear sensors with uncertainty estimates from Gaussian Process Regression, Journal of Low Temperature Physics, [online], https://doi.org/10.1007/s10909-022-02740-w, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933277
(Accessed October 8, 2025)