论文标题
基于机器学习方法的黑子组的参数化
Parametrization of sunspot groups based on machine learning approach
论文作者
论文摘要
在白光中观察到的黑子组作为复杂的结构。这些结构的分析通常基于简单的形态描述符，仅捕获通用属性，而错过了有关细节的信息。我们提出了一种机器学习方法，以引入对黑子组的完整而紧凑的描述。这个想法是将Sunspot图像映射到适当的低维（潜在）空间中。我们应用了变量自动编码器和主组件分析的组合，以获得一组285个潜在描述符。我们证明标准描述符被嵌入潜在的描述符中。因此，潜在特征可以被视为对黑子组的扩展描述，我们认为可以扩大有关黑子群体研究的可能性。特别是，我们证明了估计黑子组复杂性的应用。所提出的参数化模型是通用的，可以应用于在各种光谱线中观察到的其他太阳活性痕迹的研究。这项工作的关键组成部分是参数化模型，黑子组和潜在向量的数据集，可在public github存储库github.com/observethesun/sunspot_groups中获得，可用于重现结果并进行进一步研究。
Sunspot groups observed in white-light appear as complex structures. Analysis of these structures is usually based on simple morphological descriptors which capture only generic properties and miss information about fine details. We present a machine learning approach to introduce a complete yet compact description of sunspot groups. The idea is to map sunspot group images into an appropriate lower-dimensional (latent) space. We apply a combination of Variational Autoencoder and Principal Component Analysis to obtain a set of 285 latent descriptors. We demonstrate that the standard descriptors are embedded into the latent ones. Thus, latent features can be considered as an extended description of sunspot groups and, in our opinion, can expand the possibilities for the research on sunspot groups. In particular, we demonstrate an application for estimation of the sunspot group complexity. The proposed parametrization model is generic and can be applied to investigation of other traces of solar activity observed in various spectrum lines. Key components of this work, which are the parametrization model, dataset of sunspot groups and latent vectors, are available in the public GitHub repository github.com/observethesun/sunspot_groups and can be used to reproduce the results and for further research.
