Machine learning-aided line intensity ratio technique applied to deuterium plasmas

It has been demonstrated that the electron density, ne, and temperature, T-e, are successfully evaluated from He I line intensity ratios coupled with machine learning (ML). In this paper, the ML-aided line intensity ratio technique is applied to deuterium (D) plasmas with 0.031 < n(e) (10(18) m(-3)) < 0.67 and 2.3 < T-e (eV) < 5.1 in the PISCES-A linear plasma device. Two line intensity ratios, D alpha/D gamma and D alpha/D ss, are used to develop a predictive model for ne and Te separately. Reasonable agreement of both ne and Te with those from single Langmuir probe measurements is obtained at n(e) > 0.1 x 10(18) m(-3). Addition of the D2/D alpha intensity ratio, where the D-2 band emission intensity is integrated in a wavelength range of lambda similar to 557.4-643.0 nm, is found to improve the prediction of, in particular, n(e), and T-e. It is also confirmed that the technique works for D plasmas with 0.067 < ne (10(18) m(-3)) < 6.1 and 0.8 < T-e (eV) < 15 in another linear plasma device, PISCES-RF. The two training datasets from PISCES-A and PISCES-RF are combined, and unified predictive models for ne and Te give reasonable agreement with probe measurements in both devices.