Integration of ML methods with CR model-based optical diagnostic for the estimation of electron temperature in Ga laser produced plasma

Accelerated diagnostic of plasma plays a significant role in controlling and optimizing plasma-mediated processing, particularly for plasma with higher temporal and spatial gradients, such as laser produced plasma (LPP). In the present work, two advanced machine learning (ML) algorithms, random forest regression, and gradient boosting regression are integrated with noninvasive collisional radiative (CR) model-based optical diagnostics to facilitate accurate diagnostics. A comprehensive fine-structure resolved CR model framework is developed by incorporating our consistent cross section data obtained from the Relativistic Distorted Wave method [Suresh et al., “Fully relativistic distorted wave calculations of electron impact excitation of gallium atom: Cross sections relevant for plasma kinetic modelling,” Spectrochim. Acta B: At. Spectrosc. 213, 106860 (2024)]. An extensive dataset of CR model simulated intensities is created to train and test the ML methods. The present CR model is applied to characterize the Gallium LPP coupling with the optical emission spectroscopic measurements of Guo et al. [“Time-resolved spectroscopy analysis of Ga atom in laser induced plasma,” Laser Phys. 19, 1832–1837 (2009)] at different delay times. Further, a detailed correlation study of the line intensity ratios is performed to observe the qualitative behavior of the plasma parameters. The electron temperature results obtained from the CR model, ML, and line ratio methods were compared and found to be in excellent agreement. Overall, the present study demonstrates diagnostic approaches that can benefit the LPP community significantly by providing a rapid understanding of the plasma behavior across various operating conditions.