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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.
Citation
Journal of Low Temperature Physics
Volume
209

Keywords

Microcalorimeters, X-ray pulses, Detector calibration

Citation

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 June 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created August 22, 2022, Updated January 26, 2023