A neural network for the analysis of Langmuir-probe characteristics

Abstract

Langmuir probes have been widely used in the field of plasma diagnostics for the characterisation of plasma properties. These probes play a crucial role in understanding the behaviour of a diverse range of plasmas, e.g. edge plasmas in fusion experiments. The measurement of electron density (n_e) and electron temperature (T_e) provides valuable insights into the plasma’s state, stability, and confinement properties. Conventionally, this analysis involves post-experiment fitting methods to calculate plasma properties from the measured current–voltage curves obtained from Langmuir probes. This work introduces a neural-network approach for analysing probe data from the TJ-K stellarator, allowing for fast associative plasma characterisation. The results show a reliable performance on test data within the domain of the training set, predicting both n_e and T_e within the 10 % intrinsic error. Performance on unseen data outside the domain of the training set was on average within a 26 % and 21 % error on n_e and T_e, respectively. The network’s further abilities, including the identification of low-quality and falsely-labelled data, were also explored. The use of neural networks (NNs) offers fast predictions, enabling further research into real-time applications and live feedback control. This paper highlights the promising role of NNs in enhancing the analysis of Langmuir-probe characteristics.