基于多实例学习的可解释太阳耀斑预测
Using Multiple Instance Learning for Explainable Solar Flare Prediction
摘要：
在这项工作中，我们利用弱标记的光谱数据集从美国宇航局IRIS卫星的预测太阳耀斑使用多实例学习（MIL）的范例。虽然标准的监督学习模型期望每个实例都有一个标签，但MIL放松了这一点，只考虑要标记的实例。这非常适合于使用IRIS数据进行耀斑预测，IRIS数据由沿仪器狭缝测量的紫外光谱袋的时间序列组成。特别是，我们认为读出窗口周围的Mg II H&K线，编码信息的太阳色球层的动态。我们的MIL模型不仅能够预测未来$\sim$ 25分钟内是否会发生耀斑，准确率约为90%，而且还能够解释哪些光谱轮廓对其袋级预测特别重要。这些信息可用于突出正在进行的IRIS观测中的实时感兴趣区域，并确定典型耀斑前兆光谱剖面的候选者。我们使用k-均值聚类来提取与耀斑预测相关的光谱轮廓组。恢复组显示高强度，三重红翼发射和单峰的H和K线，发现由以前的工作。它们似乎与小规模的爆炸事件有关，据报道，这些事件发生在耀斑爆发前几十分钟。
英文摘要：
In this work we leverage a weakly-labeled dataset of spectral data from NASAs IRIS satellite for the prediction of solar flares using the Multiple Instance Learning (MIL) paradigm. While standard supervised learning models expect a label for every instance, MIL relaxes this and only considers bags of instances to be labeled. This is ideally suited for flare prediction with IRIS data that consists of time series of bags of UV spectra measured along the instrument slit. In particular, we consider the readout window around the Mg II h&k lines that encodes information on the dynamics of the solar chromosphere. Our MIL models are not only able to predict whether flares occur within the next $\sim$25 minutes with accuracies of around 90%, but are also able to explain which spectral profiles were particularly important for their bag-level prediction. This information can be used to highlight regions of interest in ongoing IRIS observations in real-time and to identify candidates for typical flare precursor spectral profiles. We use k-means clustering to extract groups of spectral profiles that appear relevant for flare prediction. The recovered groups show high intensity, triplet red wing emission and single-peaked h and k lines, as found by previous works. They seem to be related to small-scale explosive events that have been reported to occur tens of minutes before a flare.
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