Machine learning-based multi-classification of space signals from the solar orbiter mission

Abstract This study focuses on the classification of space signals detected by the Solar Orbiter mission. Due to the complex space environment, dust impacts and natural waves are crucial to understand. Dust impacts have been studied using various methods and instruments over the years. Langmuir waves and electrostatic solitary waves (ESWs) are also important phenomena in space plasmas. This study considers four classes: non-dust signals, dust signals, Langmuir waves, and ESWs. The study uses Solar Orbiter data from November 2022 to September 2023. The data is preprocessed to reduce noise and improve its quality. We utilize the inverse Fourier transform for reconstructing signals, median filtering for noise reduction, resampling to achieve uniformity, and data augmentation to increase the diversity of the dataset. The dataset contains 4800 samples, which are divided into training, validation, and test sets. Multiple classification models are utilized, among which the multiscale residual network (MSRN) performed excellently, achieving the highest performance with a classification precision of 99.37%. The MSRN model shows superior performance with high accuracy, recall, and F1-scores. The confusion matrix and t-SNE visualization further confirm the model’s effectiveness in capturing signal features and distinguishing different classes.