Applicability of neural networks to etalon fringe filtering in laser spectrometers. (May 2018)

• Record Type: Journal Article
• Authors: Nicely, J.M.; Hanisco, T.F.; Riris, H.
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 211(2018)
• Page Start: 115
• Page End: 122
• Publication Date: 2018-05
• Subjects: Neural networks -- Diode laser spectroscopy -- Etalon fringes -- Atmospheric retrievals; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2018.03.004
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: Etalon filtering in laser spectrometry is a challenge, can lead to retrieval biases. We compare least squares & neural network fits with synthetic absorption lineshapes. Neural networks outperform least squares fit in a variety of cases. Abstract: We present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.