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The solar wind consists of charged particles ejected from the Sun intointerplanetary space and towards Earth. Understanding the magnetic field of thesolar wind is crucial for predicting future space weather and planetaryatmospheric loss. Compared to large-scale magnetic events, smaller-scalestructures like magnetic discontinuities are hard to detect but entailimportant information on the evolution of the solar wind. A lack of labeleddata makes an automated detection of these discontinuities challenging. Wepropose Deep-SWIM, an approach leveraging advances in contrastive learning,pseudo-labeling and online hard example mining to robustly identifydiscontinuities in solar wind magnetic field data. Through a systematicablation study, we show that we can accurately classify discontinuities despitelearning from only limited labeled data. Additionally, we show that ourapproach generalizes well and produces results that agree with experthand-labeling.

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