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Pulsed plasma discharges, such as the plasma focus, are a source of pulsed X rays, therefore it is desirable to understand the relationship between this fast transient phenomena and the electrical variables of the discharge. Parameters from the electrical diagnostic signals are typically used to characterize the plasma focus discharge and for the correlations with X rays measurements via scatter plots. To further evaluate relevant information in the electrical signals, besides the characteristic parameters, an implementation of different types of machine learning algorithms, that included deep learning, was performed. A classification of pulses associated with an X rays measurement, in terms of the electrical signals data as input, was carried out. Two approaches were compared: the selection of the characteristic parameters and the use of the entire signals so the algorithms could find additional information for the classification task. The electrical diagnostic signals corresponded to: the voltage at the electrodes of the discharge chamber measured with a resistive voltage divider; time variation of the circuit current measured with a Rogowski coil and an inductive loop sensor; and the electromagnetic burst from the circuit measured with a Vivaldi antenna. The X rays measurement corresponded to the signal obtained from a scintillator-photomultiplier. In terms of the performance of the algorithms models in this classification problem, the results indicated that there is no significative improvements when using the entire signal or the selection of characteristic parameters. The best results were obtained when the following parameters were used: voltage at time of gas breakdown, voltage at time of pinch, current at time of pinch, time derivative of current at time of pinch, time from breakdown to pinch, and the Fast Fourier Transform of the part of the Vivaldi antenna signal related to the pinch event.

Inference of X-Ray Emission From a Plasma Focus Discharge: Comparison Between Characteristic Parameters and Neural Network Analyses