利用LAMOST搜寻热亚矮星--基于光谱数据的搜寻方法与参数估计算法研究

热亚矮星是一类特殊恒星，它的研究与超新星前身星、引力波源等的研究紧密相关。在LAMOST光谱中搜寻热亚矮星将极大扩展其样本数量，对热亚矮星的理论研究和统计性质研究具有重要意义。由于LAMOST没有一致的测光数据，传统的基于颜色判据搜寻热亚矮星的方法不适用于LAMOST光谱。本项目将针对LAMOST数据特征，研究新的基于非测光数据的搜寻方法，主要内容包括（1）光谱数据的预处理：对光谱进行小波分析等适当的预处理，减少噪声干扰，提高搜寻效率；（2）热亚矮星搜寻算法研究：研究基于深度学习等机器学习算法的热亚矮星搜寻算法；（3）热亚矮星元素丰度估计：使用新算法以及即将发布的LAMOST中分辨率光谱估计热亚矮星元素丰度，提供可靠的元素丰度信息。本项目将为搜寻热亚矮星并进行参数估计提供新的基于非测光数据的方法，同时提供高质量的热亚矮星光谱与元素丰度，并且有希望在特殊热亚矮星搜寻方面有一些重要发现。

Hot subdwarf stars are core He burning stars located at the blue end of the horizontal branch, which is also known as the extreme horizontal branch. The study of hot subdwarf stars is important for understanding stellar astrophysics, galaxies and gravitational wave source. Presently, some problems associated with hot subdwarf stars are still unclear. To better study the properties of these stars, we should find more hot subdwarf stars to enlarge the sample size. The traditional method of searching for hot subdwarfs from the large data sets is based on the color cuts followed by visual inspection. However, this method is not suitable for the data set without homogeneous colors, such as the spectra obtained by the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST). Therefore, this project will study a new method of searching for hot subdwarf stars from LAMOST based on spectral data. The main contents include (1) spectral preprocessing: include the spectral denoising and cleaning to improve searching efficiency; (2) machine learning algorithms used to search for hot subdwarf stars: we will study suitable classification algorithm for the LAMOST spectra data to find hot subdwarf stars efficiently; (3) determine the elemental abundance of hot subdwarf stars: we will determine the elemental abundance reliably by using the medium-resolution spectra of LAMOST that will be released lately. This project will provide new methods of searching for hot subdwarf stars from the spectral data, and provide reliable elemental abundance of hot subdwarf stars.