机器学习在太阳光谱图自动噪声处理中的应用
Machine Learning in Application to Automatic Noise Processing of Solar Spectrograms
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中文总结 AI 辅助
该研究开发了用于太阳光谱图噪声处理的机器学习方法，通过卷积神经网络实现光谱转换与杂质自动去除，可提升可分析光谱比例，为太阳物理模型构建提供更多数据支持。
中文摘要 AI 辅助
本文开发了一种用于处理太阳光谱数据的机器学习方法，并通过1981年7月17日的边缘太阳耀斑示例验证了其性能。结果表明，机器学习可有效用于不同数字化光谱间的转换、填补独特实验数据的空白以及进行光谱清洗。具体而言，研究设计了卷积神经网络（CNN），用于在太阳耀斑光谱图的反射扫描与透射扫描之间进行转换，这一技术可便捷处理独特太阳事件的光谱图，显著提高可进一步分析的观测光谱比例。后续研究中，研究人员可可靠纳入光谱图边缘等因现有处理技术局限曾被认为可靠性不足的数据，从而增加特定观测事件的光谱线研究数量，这对构建物理模型至关重要，因为光谱特性有望在不同光谱线间一致体现。所开发方法还显著促进光谱图中杂质的检测与去除：此前需人工逐一检查光谱各特征的完整性，若为划痕、尘埃颗粒等杂质则予以排除；采用所提出的光谱图处理方案（包括用多种不同技术扫描光谱图，再利用机器学习进行对比）后，杂质排除过程可实现自动化，且被排除影响的光谱区域可被恢复，以便后续分析。
英文摘要
In this paper, a machine learning approach for processing solar spectral data were developed. Its performance was demonstrated using the example of the limb solar flare on July 17th, 1981. The results indicated that machine learning can be effectively utilized to transform between differently digitized spectra, fill gaps in unique experimental data, and undertake spectrum cleaning. Specifically, convolutional neural networks were devised to transform between reflective and transmissive scans of a solar flare spectrogram. This can be a convenient technique for treating the spectrograms of unique solar events, most notably increasing the proportion of observational spectra that can be further analyzed. Namely, in subsequent research, we will be able to confidently incorporate data such as that captured on the edges of spectrograms, which was previously deemed insufficiently reliable due to limitations of available processing techniques. This will consequently increase the number of spectral lines studied for certain observed events, which is paramount for constructing physical models, as the spectral peculiarities are expected to manifest consistently across different spectral lines. The developed approach also notably facilitates the detection and removal of impurities in the spectrograms. Previously, each distinct feature in the spectra was manually scrutinized to check its integrity in order to be excluded if identified as an impurity, such as a scratch or a dust particle. By employing the suggested protocol for treating the spectrogram, which includes scanning the spectrogram using multiple distinct techniques and then leveraging machine learning for comparison, the process of excluding impurities can now be automated. Furthermore, the spectrum areas affected by such exclusions can be restored, enabling further analysis.
