Reconstructing spectral shapes with GAN models: A data-driven approach for high-resolution spectra from low-resolution spectrometers

This study presents the development of a generative adversarial network (GAN) to generate high-resolution (HR) spectra from low-resolution (LR) spectra. Plasma emissions with second positive system of nitrogen are used for demonstration. Specair™ is used to generate HR and LR spectra pairs as the training data covering the range of rotational temperatures (T_rot) and vibrational temperatures (T_vib) ranging from 300 to 1200 K and 2000 to 6500 K, respectively. Optical emission spectra from low-pressure and atmospheric-pressure plasmas are used as the testing data to show the feasibility of the model for generating HR spectra with spectra acquired using LR spectrometers. Feature matching is used during the training stage to tackle the instability issues. The distributions of the discriminator scores are used as an initial criterion to monitor the training procedure. The results show a weighted coefficient of determination (${\bar{R}}^2$) greater than 0.9999 between the simulated and generated HR spectra. The fitting errors for T_rot and T_vib between generated HR spectra and experimental HR spectra acquired from an HR spectrometer are mostly below 5 %. The results indicate that this GAN serves as an efficient approach to obtain HR spectra when HR spectrometers are not available.