Machine learning with domain adaptation techniques for the detection of plasmoids in MMS data

We present a method that utilizes domain adaptation techniques to adapt convolutional neural network (CNN)-based classifiers trained on particle-in-cell (PIC) data to Magnetospheric Multiscale (MMS) data. A 1D CNN-based model consisting of a feature extractor and classifier is trained on 1D slices of 2D PIC simulations to detect plasmoids. A data pipeline is used to select, clean, and process MMS data from the magnetotail. The adversarial discriminative domain adaptation (ADDA) method is implemented to construct a feature extractor for the MMS data which produces features similar to the CNN-based model's feature extractor for the PIC data. This generates an adapted model which attempts to detect plasmoids in MMS data which consists of the MMS feature extractor followed by the classifier layers from the original simulation-trained model. The results of the model pre-and-post adaptation applied to an existing catalog of magnetotail plasmoids are presented and discussed.