Deep-SWIM: A few-shot learning approach to classify Solar WInd Magnetic field structures

TL;DR

Deep-SWIM is a novel few-shot learning method that effectively detects small-scale magnetic discontinuities in solar wind data, aiding space weather prediction with limited labeled data.

Contribution

It introduces a contrastive learning-based approach with pseudo-labeling and hard example mining for classifying solar wind magnetic structures with minimal labeled data.

Findings

Accurately classifies magnetic discontinuities with limited labels.

Generalizes well and aligns with expert annotations.

Robust detection despite scarce labeled data.

Abstract

The solar wind consists of charged particles ejected from the Sun into interplanetary space and towards Earth. Understanding the magnetic field of the solar wind is crucial for predicting future space weather and planetary atmospheric loss. Compared to large-scale magnetic events, smaller-scale structures like magnetic discontinuities are hard to detect but entail important information on the evolution of the solar wind. A lack of labeled data makes an automated detection of these discontinuities challenging. We propose Deep-SWIM, an approach leveraging advances in contrastive learning, pseudo-labeling and online hard example mining to robustly identify discontinuities in solar wind magnetic field data. Through a systematic ablation study, we show that we can accurately classify discontinuities despite learning from only limited labeled data. Additionally, we show that our approach generalizes well and produces results that agree with expert hand-labeling.