Gaussian process regression-based spectral modeling algorithm and error compensation method for echelle spectrometers

Longfei Han

doi:10.1016/j.sab.2025.107288

Gaussian process regression-based spectral modeling algorithm and error compensation method for echelle spectrometers

2025-07-31

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DOI：10.1016/j.sab.2025.107288

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Abstract

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• A two-dimensional spectral model for the echelle spectrometer was established using Gaussian process regression algorithms. • A dual centroid algorithm extracts characteristic wavelength centroids from unexposed calibration sources. • Polynomial fitting was applied to quantify deviations between the theoretical model and experimental data. • Model precision was validated using Ne lines (long-wavelength) and Cu/Sr/Na lines (short-wavelength).

Keywords:

echelle spectrometer

Gaussian process regression

centroid algorithm

polynomial fitting

wavelength calibration

Gaussian process regression-based spectral modeling algorithm and error compensation method for echelle spectrometers

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