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Description
While the helium line intensity ratio method has been used to measure electron, ne, and temperature, $T_e$, by combining measured line intensities with a collisional radiative model (CRM) [1], one of its difficulties is to include the photon transport and metastable atom transport. A machine learning (ML) approach has been considered as an alternative method to measure $n_e/T_e$ from the line ratios. If training data is sufficiently available, it can be a useful diagnostic tool. The challenging issue in this approach is developing a global model that can be applied to other devices. In this study, we collected an OES dataset and $n_e/T_e$ data from four linear divertor simulators, and we investigated the fine-tuning method to develop a global cross-machine model.
Data from the following four linear devices are used: Magnum-PSI, NAGDIS-II, and PISCES-A, and Lotus-I. Line emissions at 447.1, 492.2, 501.6 + 504.8, 667.8, 706.5, and 728.1 nm are used. The dataset includes 24, 64, 6, and 3 discharges (radial profiles) and 960, 417, 342, and 70 data points from Magnum-PSI, NAGDIS-II, PISCES-A, and Lotus-I, respectively. Laser Thomson scattering was used in Magnum-PSI and a Langmuir probe was used in the other devices to obtain $n_e/T_e$. In addition to a deep neural network (DNN) model, physics-informed ML approach [2] was also tested, where a pre-trained NN with a CRM tuned with experimental data [3].
It was shown that a DNN model trained with the dataset from three devices leads to an error of ~100%, when applying it to a remaining unseen fourth device for both ne and $T_e$, which is significantly higher than the model applied to the seen devices. This is primarily due to device-specific parameters such as plasma radius and different ranges of ne and $T_e$, which hinder the model’s generalizability across devices. To address this, we additionally performed fine-tuning using data from the target device itself. It was found that errors significantly decreased by the fine-tuning process with a small amount of data. Furthermore, the reduction in the errors was more significant for physics-informed models. The results suggested that the physics-informed model has an advantage when using fine-tuning with a limited dataset.
[1] S. Kajita, D. Nishijima, Journal of Physics D: Applied Physics 57 (2024), 423003.
[2] S. Kajita, https://arxiv.org/abs/2506.20117.
[3] M. Goto, J. Quantitative Spectroscopy and Radiative Transfer 76 (2003) 331.