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 April 26, 2024)
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Energy calibration of nonlinear sensors with uncertainty estimates from Gaussian Process Regression
Published
Author(s)
, , , ,
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.Citation
Journal of Low Temperature Physics
Volume
209
Pub Type
Journals
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Keywords
Microcalorimeters, X-ray pulses, Detector calibration
Citation
Created August 22, 2022, Updated January 26, 2023