一种基于主分量分析的减天光方法

天光是天体观测中的一种重要噪声源.减天光问题是制约多目标光纤光谱观测深度的重要因素.主分量分析(PCA)是统计学的一种分析方法,它可以用来寻找各个天光谱之间的关系,以进一步获得目标光谱中含有的天光成分.为了研究LAMOST的减天光方法,用SDSS的一组原始观测数据进行了仿真实验,实验结果表明,采用PCA方法比SDSS处理程序能够更有效地减天光.最后对PCA方法在LAMOST中的应用前景进行了展望.

A Sky-subtraction Method Based on Principal Component Analysis

Sky is an important noise in astronomical observations.The problem of sky-subtraction is an important factor that restricts the depth of the multi-object fiber spectroscopic observation.The method of principle component analysis(PCA)comes from statistics and it can be used to find the relation among the entire sky spectra which far- ther can be used to obtain the sky contents of the object spectrum.Using PCA method, several main sky components can be obtained from a certain observation and the content of each components in each object spectrum can be measured.By using the MDL or AIC criterion from the field of signal detection,the number of main components can be decided. Thus the sky contents of each object spectrum can be determined and be subtracted to get the original object spectrum.For studying the way of the sky-subtraction of LAMOST,a group of observation data from SDSS is verified with PCA.The experimental result shows that method of PCA is more effectual in sky-subtraction than SDSS reduction.In the end, prospect of the use of PCA in LAMOST is anticipated.