Plasma‐Sheet Bubble Identification Using Multivariate Time Series Classification

Abstract

AbstractPlasma‐sheet bubbles play a major role in the earthward transport of magnetotail particles. The most remarkable feature of bubbles is their fast bulk flow velocities, along with reduced plasma density and pressure accompanied by magnetic field dipolarization. These bubbles can be identified based on in situ observations, but subjective ambiguity necessitates human verification, due to confusion with other phenomena mostly associated with magnetic reconnection and plasma waves. In this study, we aim to employ machine learning (ML) techniques to detect bubbles automatically and to create a tool that can be utilized by individuals without specialized subject expertise. To identify bubbles, we combine three distinct techniques: MINImally RandOm Convolutional KErnel Transform (MINIROCKET), 1D convolution neural network, and Residual Network (ResNet). The imbalanced training data set consists of bubble and non‐bubble events with a ratio of 1:40 from 2007 to 2020. The results indicate that the accuracy of all three models is approximately 99%, and their precision, recall, and F2 score are all above 80% for both the validation and test datasets. The three methods are combined with the intersection set as the minimum set of predictions and the union set as the maximum set. The union set can accurately identify 66.7% of bubbles. The combined method reduces the number of false negatives significantly. In the prediction of bubbles in observations made in the year 2021 using a union set, the bubbles obtained by the model are comparable to those discovered using traditional criteria and manual inspections.