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[郭守敬望远镜(LAMOST)光谱的大气参数估计]

[Atmospheric parameter estimation for LAMOST/GUOSHOUJING spectra].

作者信息

Lu Yu, Li Xiang-Ru, Yang Tan

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Nov;34(11):3127-31.

Abstract

It is a key task to estimate the atmospheric parameters from the observed stellar spectra in exploring the nature of stars and universe. With our Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) which begun its formal Sky Survey in September 2012, we are obtaining a mass of stellar spectra in an unprecedented speed. It has brought a new opportunity and a challenge for the research of galaxies. Due to the complexity of the observing system, the noise in the spectrum is relatively large. At the same time, the preprocessing procedures of spectrum are also not ideal, such as the wavelength calibration and the flow calibration. Therefore, there is a slight distortion of the spectrum. They result in the high difficulty of estimating the atmospheric parameters for the measured stellar spectra. It is one of the important issues to estimate the atmospheric parameters for the massive stellar spectra of LAMOST. The key of this study is how to eliminate noise and improve the accuracy and robustness of estimating the atmospheric parameters for the measured stellar spectra. We propose a regression model for estimating the atmospheric parameters of LAMOST stellar(SVM(lasso)). The basic idea of this model is: First, we use the Haar wavelet to filter spectrum, suppress the adverse effects of the spectral noise and retain the most discrimination information of spectrum. Secondly, We use the lasso algorithm for feature selection and extract the features of strongly correlating with the atmospheric parameters. Finally, the features are input to the support vector regression model for estimating the parameters. Because the model has better tolerance to the slight distortion and the noise of the spectrum, the accuracy of the measurement is improved. To evaluate the feasibility of the above scheme, we conduct experiments extensively on the 33 963 pilot surveys spectrums by LAMOST. The accuracy of three atmospheric parameters is log Teff: 0.006 8 dex, log g: 0.155 1 dex, [Fe/H]: 0.104 0 dex.

摘要

从观测到的恒星光谱估计大气参数是探索恒星和宇宙本质的一项关键任务。借助于我们的大天区多目标光纤光谱望远镜(LAMOST),它于2012年9月开始正式巡天观测,我们正以前所未有的速度获取大量恒星光谱。这给星系研究带来了新的机遇和挑战。由于观测系统的复杂性,光谱中的噪声相对较大。同时,光谱的预处理程序也不理想,如波长校准和流量校准。因此,光谱存在轻微畸变。这些导致了测量的恒星光谱估计大气参数的高难度。估计LAMOST大量恒星光谱的大气参数是重要问题之一。本研究的关键是如何消除噪声并提高测量恒星光谱大气参数的准确性和稳健性。我们提出了一种用于估计LAMOST恒星大气参数的回归模型(SVM(lasso))。该模型的基本思想是:首先,我们使用哈尔小波对光谱进行滤波,抑制光谱噪声的不利影响并保留光谱的最具判别力信息。其次,我们使用lasso算法进行特征选择并提取与大气参数高度相关的特征。最后,将这些特征输入到支持向量回归模型中以估计参数。由于该模型对光谱的轻微畸变和噪声具有更好的容忍性,测量精度得到了提高。为了评估上述方案的可行性,我们对LAMOST的33963条先导巡天光谱进行了广泛实验。三个大气参数的精度为:有效温度对数log Teff:0.0068 dex,表面重力对数log g:0.1551 dex,铁丰度[Fe/H]:0.1040 dex。

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