基于神经网络的恒星大气参数自动测量

Automatic prediction of stellar atmospheric parameters with neural network

提出了一种基于神经网络的恒星光谱大气参数的自动测量方法,该方法能够根据恒星的光谱自动得到恒星的三个重要参数:有效温度Teff,表面重力logg和金属丰度[Fe/H]的估计值。首先对实测恒星光谱进行预处理,包括小波去噪,光谱波长统一化;然后通过对训练样本进行PCA分析获得特征空间变换矩阵进行数据降维;最后通过训练好的神经网络得到参数的估计值。实验结果分析表明,该方法比其它估计方法如非参数估计、支持向量回归和偏最小二乘回归具有更高的测量精度。

An automatic method to predict stellar atmospheric parameters using artificial neural network （ANN） was presented, which can achieve the prediction of stellar effective temperature Teff, surface gravity log g and metallic abundance. The method is composed of three functional units, namely stellar spectra preprocessing （include stellar spectra denoise and wavelength normalization）, data reduction （using principal components analysis） and parameters estimation using BP network. The experimental results show that the method is better than other methods such as non-parameter estimation, SVM regression and PLS regression.