用自编码器识别太阳耀斑前异常谱线，定位能量存储区。
Autoencoder-Based Detection of Anomalous Stokes V Spectra in the Flare-Producing Active Region 13663 Using Hinode/SP Observations
- 用自编码器压缩光谱数据，自动检测异常
- 发现耀斑前异常谱线集中于磁极反转线附近
- 适合研究太阳耀斑预警与空间天气预报
在太阳耀斑事件中，从观测光谱中识别异常信号对理解其物理特征至关重要。然而，传统基于物理模型的光谱分析方法难以处理海量噪声大、结构复杂的观测数据。为此，我们采用深度学习方法，利用Hinode/SP仪器的斯托克斯V谱数据，构建自编码器模型实现光谱压缩，并作为异常检测工具。该模型成功识别出2024年5月5日发生在太阳活动区13663（NOAA AR 13663）的X1.3级耀斑爆发前的异常光谱点。这些异常谱线表现出高度复杂轮廓，且在磁图图像中与磁极反转线空间重合，提示其可能为磁能积累区域及耀斑触发源。值得注意的是，这些异常极为局部化，现有手动方法难以在磁图中有效捕捉。
原文摘要 · Abstract (English)
Detecting unusual signals in observational solar spectra is crucial for understanding the features associated with impactful solar events, such as solar flares. However, existing spectral analysis techniques face challenges, particularly when relying on pre-defined, physics-based calculations to process large volumes of noisy and complex observational data. To address these limitations, we applied deep learning to detect anomalies in the Stokes V spectra from the Hinode/SP instrument. Specifically, we developed an autoencoder model for spectral compression, which serves as an anomaly detection method. Our model effectively identifies anomalous spectra within spectro-polarimetric maps captured prior to the onset of the X1.3 flare on May 5, 2024, in NOAA AR 13663. These atypical spectral points exhibit highly complex profiles and spatially align with polarity inversion lines in magnetogram images, indicating their potential as sites of magnetic energy storage and possible triggers for flares. Notably, the detected anomalies are highly localized, making them particularly challenging to identify in magnetogram images using current manual methods.
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