Wavelength calibration with deep-learning using long short-term memory architectures

Dongyue Liu; Bryan M. Hennelly

doi:10.1117/12.2689219

27 November 2023 Wavelength calibration with deep-learning using long short-term memory architectures

Dongyue Liu, Bryan M. Hennelly

Proceedings Volume 12770, Optics in Health Care and Biomedical Optics XIII; 127703G (2023) https://doi.org/10.1117/12.2689219
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

In this paper, the long-term short-term memory (LSTM) architecture is investigated as a tool for wavelength calibration of a spectrometer. Polynomial fitting is the most common method of wavelength calibration, whereby wavelength standards, such as neon and krypton are recorded and the position of the spectral lines on the detector together with the known wavelengths are used for fitting. The method performs poorly when only a small number of lines appear within the bandwidth recorded by the spectrometer. We demonstrate how the basic encoder-decoder LSTM architecture can be used to provide superior wavelength calibration accuracy when five or less lines are present. We believe with further development, machine learning could outperform the traditional methods in all cases.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongyue Liu and Bryan M. Hennelly "Wavelength calibration with deep-learning using long short-term memory architectures", Proc. SPIE 12770, Optics in Health Care and Biomedical Optics XIII, 127703G (27 November 2023); https://doi.org/10.1117/12.2689219

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