天文观测技术的迅速发展推动了大规模的星系光谱巡天计划如SDSS、 LAMOST等,面对这些巡天项目所观测到的海量光谱数据, 研究自动的光谱分析方法已成为必然的选择。研究了基于Bayes决策的光谱分类方法, 将光谱分为恒星, 星系和类星体三类。 首先采用主分量分析来进行特征提取, 将光谱投影到由三个主分量构成的特征空间中； 然后, 采用非参数密度估计Parzen窗法来估计类条件概率密度函数； 最后利用基于最小错误率的Bayes决策进行分类。 在Parzen窗法中, 核宽很大程度上影响着估计效果, 从而影响着分类效果。 通过详尽的实验分析了核宽和分类效果的关系, 发现当核宽接近某个阈值时, 识别率将会增加,但小于这个阈值时, 识别率反而下降。

The rapid development of astronomical observation has led to many largesky surveys such as SDSS (Sloan digital sky survey) and LAMOST (large sky areamulti-object spectroscopic telescope). Since these surveys have produced verylarge numbers of spectra, automated spectral analysis becomes desirable andnecessary. The present paper studies the spectral classification method based onBayes decision theory, which divides spectra into three types: star, galaxy andquasar. Firstly, principal component analysis (PCA) is used in featureextraction, and spectra are projected into the 3D PCA feature space; secondly,the class conditional probability density functions are estimated using the non-parametric density estimation technique, Parzen window approach; finally, theminimum error Bayes decision rule is used for classification. In Parzen windowapproach, the kernel width affects the density estimation, and then affects theclassification effect. Extensive experiments have been performed to analyze therelationship between the kernel widths and the correct classification rates. Theauthors found that the correct rate increases with the kernel width being closeto some threshold, while it decreases with the kernel width being less than thisthreshold.(201008282)资助