提出一种基于BP神经网络及Ca线线指数估计恒星大气金属丰度的方法。 该方法以从斯隆数字巡天SDSS中恒星光谱以及SSPP给出的参数作为训练样本, 其中每条恒星光谱计算16个Ca线线指数, 结合其他方法得到的表面有效温度Teff作为输入, 以SSPP得到的金属丰度［Fe/H］作为输出, 对训练样本进行重采样后通过训练得到一个人工神经网络, 通过该网络可以用来预测恒星光谱的［Fe/H］。 通过相关实验表明, 提出的方法能够准确而且有效的测量出恒星光谱的［Fe/H］。

This paper presents a method to estimate stellar metallicity based on BP neural network and Ca line index. This method trains a BP ANN model from SDSS/SEGUE stellar spectra and parameters provided by SSPP. The values of Teff and the line index of Ca lines are the input of network while the ［Fe/H］ values are the oputput of the network. A set of samples are resampled from the set of all and then a network model Is trained. The network can be used to predict the stellar metallicity from low-resolution spsectra. The experiment shows that the proposed method can accurately and effectively measure the ［Fe/H］ from the stellar spectra.