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PUBLICATIONS

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
Pub Type
Journals

Download Paper

https://doi.org/10.1007/s10909-022-02740-w
Local Download

Keywords

Microcalorimeters, X-ray pulses, Detector calibration
Uncertainty quantification, Spectroscopy and Ionizing radiation metrology

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 October 27, 2025)

Issues

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Created August 22, 2022, Updated January 26, 2023
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