距离度量是光谱巡天数据处理中的一个重要研究内容, 其定义了一种不同光谱间的距离计算方法, 以此为基础可进行光谱的分类、 聚类、 参数测量及离群数据挖掘等工作。 距离度量方法的好坏在一定程度上影响了分类、 聚类、 参数测量及离群数据挖掘的效果及性能, 同时随着大规模恒星光谱巡天项目的开展, 如何针对恒星光谱定义更为有效的距离度量方法成为其数据处理中一个非常关键的问题。 基于此问题, 在充分考虑到恒星光谱的特点及其数据特征的基础上, 提出一种新的恒星光谱间的距离度量方法: 残差分布距离。 该距离度量有别于传统计算恒星光谱间距离计算方法, 利用该方法计算恒星光谱间的距离时, 首先将两条光谱归一化到同一尺度下, 然后计算对应波长处的残差, 以残差谱分布的标准差作为距离度量。 该距离度量方法可用于恒星分类、 聚类以及恒星大气物理参数测量等应用中。 本文以恒星光谱细分类为例来比较检验该距离度量方法, 结果表明该方法定义的距离在分类时能更为有效的刻画不同类别光谱间的差距, 可以很好的用于相关应用中。 同时还研究了信噪比对该距离度量方法的影响: 残差分布距离一定程度上受光谱信噪比影响, 信噪比越小, 对距离的影响越大; 在信噪比大于10之后, 残差分布距离对分类的影响很小。

Distance metric is an important issue for the spectroscopic survey data processing, which defines a calculation method of the distance between two different spectra. Based on this, the classification, clustering, parameter measurement and outlier data mining of spectral data can be carried out. Therefore, the distance measurement method has some effect on the performance of the classification, clustering, parameter measurement and outlier data mining. With the development of large-scale stellar spectral sky surveys, how to define more efficient distance metric on stellar spectra has become a very important issue in the spectral data processing. Based on this problem and fully considering of the characteristics and data features of the stellar spectra, a new distance measurement method of stellar spectra named Residual Distribution Distance is proposed. While using this method to measure the distance, the two spectra are firstly scaled and then the standard deviation of the residual is used the distance. Different from the traditional distance metric calculation methods of stellar spectra, when used to calculate the distance between stellar spectra, this method normalize the two spectra to the same scale, and then calculate the residual corresponding to the same wavelength, and the standard error of the residual spectrum is used as the distance measure. The distance measurement method can be used for stellar classification, clustering and stellar atmospheric physical parameters measurement and so on. This paper takes stellar subcategory classification as an example to test the distance measure method. The results show that the distance defined by the proposed method is more effective to describe the gap between different types of spectra in the classification than other methods, which can be well applied in other related applications. At the same time, this paper also studies the effect of the signal to noise ratio (SNR) on the performance of the proposed method. The result show that the distance is affected by the SNR. The smaller the signal-to-noise ratio is, the greater impact is on the distance; While SNR is larger than 10, the signal-to-noise ratio has little effect on the performance for the classification.