基于神经网络的模板匹配方法求正常星系红移

星系通常分为正常星系(NG)与活动星系(AG)两类。文章提出了一种自动获取NG红移的快速有效方法: (1) 由NG模板根据红移范围Ⅰ: 0.0～0.3与Ⅱ: 0.3～0.5模拟得到两类星系样本, 进行PCA变换获得样本特征向量; (2) 利用概率神经网络设计两类样本特征向量的Bayes分类器; (3) 对于实际NG光谱数据, 利用Bayes分类器进行分类确定其红移的范围, 然后在此范围内进行模板匹配得到红移的准确值。与在整个红移范围内的模板匹配方法相比, 此方法不但节省了50%的模板匹配运算量, 而且还大大提高了红移值测量的精度。文章研究结果对于大型光谱巡天所产生的海量数据的自动处理具有重要意义。

Using Neural Networks Based Template Matching Method to Obtain Redshifts of Normal Galaxies

Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is pro posed in this paper, which consists of the following three main steps: (1) Fr om the template of normal galaxy, the two sets of samples are simulated, one w ith the redshift of 0.0-0.3, the other of 0.3-0.5, then the PCA is used to extract the main components, and train samples are projected to the main com ponent subspace to obtain characteristic spectra. (2) The characteristic spectr a are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoke d to locate the redshift value within the estimated range. Compared with the tr aditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the e stimat ion accuracy. As a result, the proposed method is particularly useful for autom atic spectrum processing produced from a large-scale sky survey project.