恒星大气参数测量  

Survey of Stellar Atmosphere Parameter Estimation

作　　者：袁海龙 张彦霞[1] 张昊彤[1] 赵永恒[1] Yuan Hailong;Zhang Yanxia;Zhang Haotong;Zhao Yongheng(Key Laboratory of Optical Astronomy.National Astronomical Observatories.Chinese Academy of Sciences.Beijing 100101.China)

机构地区：[1]中国科学院光学天文重点实验室(国家天文台),北京100101

出　　处：《天文研究与技术》2018年第3期257-265,共9页Astronomical Research & Technology

基　　金：国家重点基础研究发展计划(973计划)(2014CB845700);国家自然科学基金(U1731109;11403046)资助

摘　　要：从恒星的研究意义谈起,介绍了恒星大气参数的基本概念及研究意义;阐述了恒星参数测量方法的分类:直接测量和间接测量。着重评述了间接测量方法,包括测光方法、红外流量方法、巴尔默线轮廓拟合、谱线比例方法、线指数方法、金属线诊断法、光谱模板拟合和机器学习方法等。指出在大型巡天数据中光谱模板拟合与机器学习方法的优势及其广泛应用。对于高分辨率光谱,金属线诊断仍然备受天文学家青睐;红外流量方法的测量结果常用来定标。Starting with the significance of the stellar research?the basic conception and importance of stellar atmosphere parameters are introduced.The two major classes of estimation approaches of stellar atmosphere parameters?the direct technique and the indirect technique?are discussed.This paper focuses on the indirect technique?which can be further divided into several methods:the photometric method?the infrared flux method?the Balmer profile method?the spectral line ratio method?the line index method?the metal line diagnostics method?the spectral template fitting method and the machine learning method.While the spectral template fitting method is widely used in current large spectroscopic survey projects?such as SDSS?RAVE and LAMOST?the machine learning method has shown its advantages in many specific situations.Among various machine learning methods and technologies?the most popular ones are principal component analysis(PCA)?k￣nearest neighbors(KNN)?support vector machine(SVM)?and all kinds of artificial neural network(ANN)methods.On the other hand?traditional astrophysical methods still play prominent roles.The metal line diagnostics method is still favored by astronomers when analyzing high resolution spectra.The experimental results from infrared flux method are generally used as calibration.

关 键 词：参数测量 恒星 光谱 机器学习 

分 类 号：P141.5[天文地球—天体物理]