论文标题
Deep-Swim：一种对太阳风磁场结构进行分类的几次学习方法
Deep-SWIM: A few-shot learning approach to classify Solar WInd Magnetic field structures
论文作者
论文摘要
太阳风是由从太阳弹出到星际空间并向地球的带电颗粒组成的。了解太阳风的磁场对于预测未来的太空天气和行星大气损失至关重要。与大规模磁性事件相比，很难检测到较小规模的结构，例如磁性不连续性，但需要有关太阳风演化的重要信息。缺乏标记的数据使得对这些不连续性的自动检测具有挑战性。我们提出了Deep-Swim，这是一种利用对比度学习，伪标记和在线硬示例挖掘的方法，以牢固地识别太阳风磁场数据中的不连续性。通过一项系统的消融研究，我们表明，尽管仅从有限的标记数据中学习，我们仍可以准确地对不连续性进行分类。此外，我们表明我们的方法可以很好地概括，并产生与专家手工标签一致的结果。
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.
