标题： 自组织图在恒星光谱分类中的应用 显示英文标题

标题： Application of Self-Organizing Map to Stellar Spectral Classifications

摘要： 我们提出了一种自动、快速、准确且鲁棒的天体分类方法。 自组织映射（SOM）作为一种无监督的人工神经网络（ANN）算法，用于恒星光谱的分类。 SOM用于对Jacoby、Hunter和Christian（JHC）库的不同光谱类别进行聚类。 这种ANN技术不需要训练样本，恒星光谱数据集直接输入到网络中进行分类。 JHC库包含161个光谱，其中158个光谱被选用于分类。 这158个光谱作为网络的输入向量，并映射到二维输出网格中。 彼此接近的输入向量在输出空间中被映射到相同或相邻的神经元。 因此，相似的对象在输出图中形成聚类，使得高维数据的分析变得容易。 在对158个恒星光谱（每个包含2799个数据点）运行SOM算法后，分析输出图发现，输出图中有7个聚类，对应O到M型恒星。 但其中有12个误分类，所有这些都被错误地归入正确聚类的邻近区域，成功率为约92.4%。 显示英文摘要

摘要： We present an automatic, fast, accurate and robust method of classifying astronomical objects. The Self Organizing Map (SOM) as an unsupervised Artificial Neural Network (ANN) algorithm is used for classification of stellar spectra of stars. The SOM is used to make clusters of different spectral classes of Jacoby, Hunter and Christian (JHC) library. This ANN technique needs no training examples and the stellar spectral data sets are directly fed to the network for the classification. The JHC library contains 161 spectra out of which, 158 spectra are selected for the classification. These 158 spectra are input vectors to the network and mapped into a two dimensional output grid. The input vectors close to each other are mapped into the same or neighboring neurons in the output space. So, the similar objects are making clusters in the output map and making it easy to analyze high dimensional data. After running the SOM algorithm on 158 stellar spectra, with 2799 data points each, the output map is analyzed and found that, there are 7 clusters in the output map corresponding to O to M stellar type. But, there are 12 misclassifications out of 158 and all of them are misclassified into the neighborhood of correct clusters which gives a success rate of about 92.4%.