  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.
