Machine learning assisted optical emission spectroscopy to determine electron density and electron temperature in a cascaded arc plasma

High-density cascaded arc plasma has been widely applied in linear plasma devices (LPDs), in which the laser Thomson scattering (LTS) and optical emission spectroscopy (OES) are two popular diagnostic methods for the fundamental parameters, electron density (n_e) and electron temperature (T_e). However, the complicated LTS setup lacks spatial flexibility, while the accuracy of simple OES is limited. To address this, this study develops a machine learning model based on Support Vector Machine (SVM) with a grid search optimization. This model combines the high accuracy of LTS with the spatial flexibility of OES to predict n_e and T_e in cascaded arc plasma in DUT-PSI. The model utilizes four pairs of “double-peak” spectral lines, bypassing the complicated calibration for plasma emission spectrum. The results show that when discharged conditions are included as input (Case 1), the model achieves R² values around 0.97 for n_e and about 0.92 for T_e. When excluding discharge conditions and using only line intensity ratios (LIRs) as input (Case 2), the R² values for n_e and T_e remain approximately 0.90 and 0.80, respectively. The other index, root mean square error (RMSE), follows a similar tendency to R². These findings demonstrate that the predicted n_e and T_e in both cases are highly consistent with LTS measurements. Meanwhile, sensitivity analysis reveals that the model’s prediction accuracy is robust to the specific combination of spectral lines selected in both cases. Thus, by integrating the strengths of LTS and OES, this model features calibration-free for plasma spectroscopy and flexible spectral line selection, enabling comprehensive diagnosis of cascaded arc plasma and showing potential for application in other similar LPDs.