Energy calibration of nonlinear sensors with uncertainty estimates from Gaussian Process Regression
Joseph Fowler, Bradley Alpert, Galen O'Neil, Daniel Swetz, Joel Ullom
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.
, Alpert, B.
, O'Neil, G.
, Swetz, D.
and Ullom, J.
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 December 2, 2023)