Applicability of neural networks to etalon fringe filtering in laser spectrometers. (May 2018) - Nicely, J.M.; Hanisco, T.F.; Riris, H.
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.