基于支撑矢量机的天体光谱自动分类方法

天体光谱自动识别系统的主要目标是对天体进行分类和参数测量。文章提出一种新的基于支撑矢量机的非活动天体与活动天体的自动分类方法。在信噪比低的时候 ,由于红移值未知使得噪声与发射谱线难于辨别 ,因此不能单纯依靠寻找发射谱线来确定是否为活动天体。据此 ,在低噪声情况下对非活动天体与活动天体的区分成为难点。本方法结合主分量分析法和支撑矢量机 ,能够对红移值未知的活动天体与非活动天体比较有效地进行自动光谱分类 ,对天文界的大型巡天计划中的海量观测数据自动处理有比较重要的应用价值。

Automated Classification of Celestial Spectra Based on Support Vector Machines

The main objective of an automatic recognition system of celestial objects via their spectra is to classify celestial spectra and estimate physical parameters automatically. This paper proposes a new automatic classification method based on support vector machines to separate non-active objects from active objects via their spectra. With low SNR and unknown red-shift value, it is difficult to extract true spectral lines, and as a result, active objects can not be determined by finding strong spectral lines and the spectral classification between non-active and active objects becomes difficult. The proposed method in this paper combines the principal component analysis with support vector machines, and can automatically recognize the spectra of active objects with unknown red-shift values from non-active objects. It finds its applicability in the automatic processing of voluminous observed data from large sky surveys in astronomy.