Liu Zhong-tian, Qiu Kuan-min
School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Jan;30(1):274-7.
The LAMOST project, the world's largest sky survey project being implemented in China, urgently needs an automatic stars recognition and classification system. This paper presents a method for auto-recognizing the stars based on spectral feature. This method consists of three main steps: First, the integral information of spectral lines is calculated and the stellar Balmer lines are detected by using the wavelet features of spectral lines. Then, the characteristic frequency of M-type stars and the locations of absorption bands are obtained accurately through the wavelet features of absorption bands. Finally, based on the results of the former step, the emission-line stars, M-type stars and early-type stars can be recognized. The extensive experiments with real observed spectra from the SDSS DR4 show that the method can robustly recognize stellar spectra, the correct rate of the emission-line stars is as high as 97.5%, the correct rate of M-type stars is as high as 98.1% and the correct rate of early-type stars is as high as 96.8%. The error rate of the quasars and the galaxies is less than 2%. This method is designed to automatically recognize stellar spectra with relative flux and low signal-to-noise ratio, which is applicable to the LAMOST data.
大天区面积多目标光纤光谱天文望远镜(LAMOST)项目是目前正在中国实施的世界上最大的巡天项目,迫切需要一个恒星自动识别与分类系统。本文提出了一种基于光谱特征的恒星自动识别方法。该方法主要包括三个步骤:首先,计算谱线的积分信息,并利用谱线的小波特征检测恒星巴耳末线。然后,通过吸收带的小波特征精确获得M型恒星的特征频率和吸收带位置。最后,根据前一步的结果,识别发射线恒星、M型恒星和早型恒星。利用斯隆数字巡天(SDSS)DR4的实际观测光谱进行的大量实验表明,该方法能够可靠地识别恒星光谱,发射线恒星的正确率高达97.5%,M型恒星的正确率高达98.1%,早型恒星的正确率高达96.8%。类星体和星系的错误率小于2%。该方法旨在自动识别具有相对流量和低信噪比的恒星光谱,适用于LAMOST数据。