基于非参数回归与最近邻方法的恒星光谱自动分类

An Automated Stellar Spectra Classification System Based on Non-Parameter Regression and Nearest Neighbor Method

恒星光谱数据的自动识别与分类是现代巡天望远镜所产生的海量光谱数据处理的一项重要研究内容。针对流量未定标的低分辨率恒星光谱设计了一种有效的自动分类方案,实现恒星光谱的MK分类:光谱型及其次型分类,光度型分类。该方案由三部分实现:(1)连续谱归一化:基于小波技术提取低频信号逼近连续谱的方法;(2)七种光谱型及其次型的分类通过非参数回归方法实现。(3)光度型分类通过基于最近邻的χ2方法实现。实验结果表明该方案能够有效实现恒星光谱的MK分类,光谱型及其次型的分类精度为3.2个光谱次型,Ⅰ-Ⅴ光度型的正确识别率为60%,次优统计率为78%。该方案训练速度快,方法实现容易,适用于海量恒星光谱自动分类处理系统。

The automated classification and recognition of stellar spectra is an important research for the spectra processing system of modern telescope survey project.For the spectra without flux calibration,the authors present an automated stellar spectra classification system to achieve two goals：one is the spectral class and spectral subclass classification,and the other is luminosity type recognition.The system is composed of three units：（1）continuum normalization method based on wavelet technique;（2）non-parameter regression method for spectral class and spectral subclass classification;（3）χ2 method based on nearest neighbor for luminosity type determination.The experiments on low-resolution spectra show that the system achieves 3.2 spectral subclass precision for spectral and spectral subclass classification,60% correct rate for luminosity recognition,and 78% rate for the luminosity recognition with error less than or equal to 1.The system is easy,rapid in training,and feasible for the automated spectra classification.