基于线指数的核偏最小二乘回归在恒星大气物理参数测量中的应用

恒星大气物理参数(有效温度、表面重力、化学丰度)的自动测量是天体光谱数据自动处理中的一项重要内容。由于光谱数据的高维性的特点，处理运算量非常大，对于光谱的实时分析及处理会造成延误。文章提出了一种基于Lick线指数，利用核偏最小二乘回归(KPLSR) 对恒星大气物理参数进行测量的方法。可以有效地减少运算量并可达到理想的准确率。首先计算Kurucz合成光谱的Lick线指数，利用核偏最小二乘回归方法建立Lick线指数与大气物理参数之间的核回归模型，并利用DR8实测光谱数据对得到的模型进行测试，将测试的结果与SEGUE SSPP提供的大气物理参数进行了对比，取得了比较好的效果。此外，为了检验噪声对参数测量的影响，本文还对Kurucz光谱分别加了信噪比为10, 20, 30, 40, 50, 70, 90, 120的高斯白噪声，对得到的不同信噪比的Kurucz数据进行了测试，实验结果表明，核回归模型对噪声比较敏感，光谱数据的信噪比越高，其大气物理参数的预测精度越高。提出的基于线指数建立核偏最小二乘回归模型的方法运算量小，训练速度快，适合用于恒星大气物理参数的测量。

Application of KPLSR Based on Line Index in Stellar Atmospheric Physical Parameter Measurement

In the present paper, the kernel partial least squares regression (KPLSR) method was used to measure the atmospheric physical parameters (effective temperature, surface gravity, an d chemical abundance) based on the use of Lick line index. The proposed method can reduce the computation cost and achieve an ideal measure precision. At first, the Lick indices of Kurucz synthetic spectra were extracted and the kernel regression model between the Lick indices and the atmospheric physical parameters was established using the KPLSR method. Then the physical parameters of DR8 measured spectral data were computed by the kernel regression model for testing. The test results were compared with the atmospheric physical parameters provided by SEGUE SSPP and were good results. In addition, we added a signal-to-noise ratio (SNR) of 10, 20, 30, 40, 50, 70, 90 and 120 Gaussian white noise to the Kurucz spectra. And the resulting spectra of different SNR were used to test the impact of noise on the parameter measurement. The experimental results show that the kernel regression model is sensitive to noise, the higher the SNR of spectral data, the higher the prediction accuracy of the physical parameters. The method of KPLSR based on Lick line index has small amount of computation and fast training speed, which is suitable for measuring physical parameters of stellar atmosphere.