Application of deep learning to single-shot gas-phase laser-induced breakdown spectroscopy

Single-shot fs laser-induced breakdown spectroscopy (LIBS) has the potential to capture ns-scale electrode desorption phenomena in pulsed power fusion drivers. However, the successful implementation of the diagnostic for this purpose is challenging, as it requires interpreting single-shot measurements collected from low-density gas mixtures. In this work, we demonstrate the efficacy of a Bayesian-optimized convolutional neural network (CNN) to interpret these measurements. We generated 256 distinct measurement conditions at relevant gas pressures ranging from 80-530 mTorr by mixing 100-250 sccm H₂ and 50-200 sccm CH₄ in increments of 10 sccm. Despite the considerable overlap between signals separated by 20 sccm, the CNN is able to predict the H₂ flow rate with a root-mean-square error (RMSE) of 15.9 sccm and the CH₄ flow rate with an RMSE of 12.0 sccm. The average relative prediction error is <9% for each gas and largely remains below or near 10%.