基于t分布随机邻域嵌入的高分辨率太阳Hα光谱分类
Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding
摘要：
Hα谱线是一条被充分研究的吸收线，它揭示了高度结构化和动态的太阳色球层的特性。Hα中具有独特光谱特征的典型特征包括暗条和暗斑、明亮的活动区斑、太阳黑子周围的超半影、浪涌、耀斑、埃勒曼炸弹、细丝、斑点和红斑等。这项研究是基于高光谱分辨率的Hα光谱获得的真空塔望远镜（VTT）的阶梯光栅摄谱仪位于天文台德尔泰德（ODT），特内里费岛，西班牙。t分布随机邻域嵌入（t-SNE）是一种用于非线性降维的机器学习算法。在该应用中，它将Hα光谱投影到二维地图上，在那里可以根据云模型（CM）反演的结果对光谱进行分类。CM参数光学厚度，多普勒宽度，视线速度，和源函数描述的云材料的属性。t-SNE的初步结果表明，它的强大的歧视权力，以分开安静的太阳和plage配置文件从那些适合CM反转。此外，各种t-SNE参数进行了详细的研究，看到的条件对分类的影响进行了评估，各种类型的输入数据的结果进行了比较，并确定集群与色球功能。虽然t-SNE被证明在聚类高维数据方面是有效的，但在每一步都需要人工推理来解释结果。这项探索性的研究提供了一个框架和想法，如何定制一个分类方案，对特定的光谱数据和科学问题。
英文摘要：
The Hα spectral line is a well-studied absorption line revealing properties of the highly structured and dynamic solar chromosphere. Typical features with distinct spectral signatures in Hα include filaments and prominences, bright active-region plages, superpenumbrae around sunspots, surges, flares, Ellerman bombs, filigree, and mottles and rosettes, among others. This study is based on high-spectral resolution Hα spectra obtained with the echelle spectrograph of the Vacuum Tower Telescope (VTT) located at Observatorio del Teide (ODT), Tenerife, Spain. The t-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm, which is used for nonlinear dimensionality reduction. In this application, it projects Hα spectra onto a two-dimensional map, where it becomes possible to classify the spectra according to results of Cloud Model (CM) inversions. The CM parameters optical depth, Doppler width, line-of-sight velocity, and source function describe properties of the cloud material. Initial results of t-SNE indicate its strong discriminatory power to separate quiet-Sun and plage profiles from those that are suitable for CM inversions. In addition, a detailed study of various t-SNE parameters is conducted, the impact of seeing conditions on the classification is assessed, results for various types of input data are compared, and the identified clusters are linked to chromospheric features. Although t-SNE proves to be efficient in clustering high-dimensional data, human inference is required at each step to interpret the results. This exploratory study provides a framework and ideas on how to tailor a classification scheme towards specific spectral data and science questions.
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