LAMOST恒星大气参数提取系统
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摘要
随着LAMOST银河系巡天计划的开展,每个观测夜将获得上万条恒星光谱。光谱蕴含着天体的重要信息,通过恒星光谱来得到恒星的大气物理参数是天文学中的一个基础工作,因此恒星光谱分析在天体研究中占有重要地位。通过恒星光谱快速、准确、自动的提取恒星大气物理参数是非常值得研究和探索的。本研究针对LAMOST的需求,设计、实现了一套恒星大气参数提取系统。主要研究工作如下：
1、针对LAMOST的观测光谱进行预处理,利用11条强吸收线的观测波长和实验室波长的对比,计算得到视向速度,对光谱进行视向速度校正；然后对光谱的蓝段(3850-6000 A)和红端(6000-9000 A)分别进行多项式拟合,然后再综合进行多项式拟合,提取全局连续谱；针对83条原子线和分子线进行谱线特征提取等。
2、利用网格模板匹配提取恒星大气参数。使用Kurucz模型生成覆盖网格节点的两套理论光谱模板,一套为包含g-r色指数和4400-5500 A的标准化光谱,一套只包含4400-5500 A的标准化光谱。定义观测光谱和理论模板光谱之间的距离,利用Nelder-Mead算法快速搜索极小值,利用最接近的理论光谱的参数作为观测光谱的恒星大气参数,最后利用蒙特卡洛模拟噪声的分布,得到恒星大气参数的误差。
3、使用PCA降维的恒星光谱数据作为输入,利用神经网络提取恒星大气参数。将光谱的红蓝端分别降到二十五维,作为神经网络的输入,三个恒星大气参数作为输出,中间隐藏节点为十个,构建三层神经网络。使用理论光谱和SLOAN光谱(使用SSPP测量参数)作为训练数据及测试数据,训练得到两套神经网络系统。
4、使用卡方最小化技术提取恒星大气参数。首先生成两套不同的理论光谱模板,定义观测光谱和理论光谱之间的卡方距离,为了减少计算量,利用半流量点技术来进行初始的温度估计,然后使用剪枝的多项式拟合技术得到最小值,求得有效温度,使用同样的步骤依次求得表面重力值和金属丰度值。第二套模板中使用第一套模板求得得有效温度,不过第二套模板将在以后的应用中计算alpha元素丰度。在本系统中,我们还实现了通过观测的g-r色指数和通过巴尔默(Blamer)线系的强度预测得到有效温度,最终使用了两个理论的有效温度估计和三个经验有效温度估计。
5、利用银河系中的球状星系团和疏散星团的金属丰度值对本系统的参数值准确性进行了评估,并使用其他望远镜观测的高分辨率光谱提取的参数作为真实值,对本系统中的金属丰度参数进行了校正,得到每个算法在不同区间的误差和弥散度,对结果进行了重新加权,获得了较好的准确性。
With the launch of LAMOST Survey, more than 10000 stellar spectra will be getted on each observing night. Spectra contain important informations about celestial bodies. Extraction of the atmospheric physical parameters of stars through the stellar spectra is a basic work in astronomy. The study of stellar spectra of the celestial bodies plays an important role. Numerous methods have been developed in order to extract atmospheric parameter estimates from stellar spectra in a fast, efficient, and automated way. In this study, a stellar atmospheric parameter extraction system is designed, implemented to meet the requiement of LAMOST. The main research work are as follows:
1. Preprocess for the observed spectra of LAMOST. By comparing the observation wavelength to laboratory wavelengths of 11 strong absorption lines, we can calculate the radial velocity. In order to get a good continuum fitting of 3850-9000 A, we first divide the spectrum to two parts:blue (3850-6000A) and red (6000-9000A). The blue and red part are fitted by polynomial separately, and then connected to be fitted by polynomial gain. In this system, the line indices of 83 characteristic lines are calculated for feature extraction.
2. Grid template matching is used to extract stellar atmospheric parameters. Two sets of theoretical spectra template grid are generated using the Kurucz model. One set contains the g-r color index and normalized spectrum of 4400-5500A, the other set contains only the normalized spectrum of 4400-5500A. The distance between observed spectra and the theoretical template spectrum is defined, and Nelder-Mead algorithm is used to search the minimum value. The parameters of the closest theoretical spectrum is believed to be the atmospheric parameters of the observed spectra. Monte Carlo Method is used to simulate the noise distribution, in order to obtain the error of stellar atmospheric parameters.
3. PC A is used to reduce dimensionality of stars spectral data, the Neural Network is used to extract stellar atmospheric parameters. Red and blue part of the spectrum are reduced to 25 dimensions separately.50 dimensions are regarded as the neural network's input, the three atmospheric parameters as output. A three-layer Neural Network is built with10 hidden intermediate nodes. Theoretical spectra and SLOAN spectra (measured by SSPP) are used as training data and two sets of neural network system are obtained.
4. Stellar atmospheric parameters are extracted through the chi-square minimization technique. First of all, two different sets of theoretical spectra templates are generated, and chi-square distance between the observed spectrum and theoretical spectra are defined. Half power point (HPP) is used to estimate the initial temperature to reduce the computation, and then polynomial fitting technology with pruning is used to get the minimum, so the effective temperature is obtained. Surface gravity and metal abundances values are obtained in the same way. The second set of template will use the same temperature as getted by the first set, but the second set of templates will calculate alpha element abundances in the future. In this system, effective temperature predicted from observation g-r color index and Ballmer (Blamer) lines strength are introduced. Two theoretical and three empirical temperatures estimates are obtained finally.
5. Galactic Open and Globular Clusters are used for Validation of metal abundances. The parameters extracted from high-resolution spectra of other telescope are assessed to be true values. These true values of metal abundances are used to correct the result of our system. The offset and dispersion of every algorithm are obtained. The weights are given by their offset and dispersion, and new results are re-weighted to obtain a good accuracy.
1、针对LAMOST的观测光谱进行预处理,利用11条强吸收线的观测波长和实验室波长的对比,计算得到视向速度,对光谱进行视向速度校正；然后对光谱的蓝段(3850-6000 A)和红端(6000-9000 A)分别进行多项式拟合,然后再综合进行多项式拟合,提取全局连续谱；针对83条原子线和分子线进行谱线特征提取等。
2、利用网格模板匹配提取恒星大气参数。使用Kurucz模型生成覆盖网格节点的两套理论光谱模板,一套为包含g-r色指数和4400-5500 A的标准化光谱,一套只包含4400-5500 A的标准化光谱。定义观测光谱和理论模板光谱之间的距离,利用Nelder-Mead算法快速搜索极小值,利用最接近的理论光谱的参数作为观测光谱的恒星大气参数,最后利用蒙特卡洛模拟噪声的分布,得到恒星大气参数的误差。
3、使用PCA降维的恒星光谱数据作为输入,利用神经网络提取恒星大气参数。将光谱的红蓝端分别降到二十五维,作为神经网络的输入,三个恒星大气参数作为输出,中间隐藏节点为十个,构建三层神经网络。使用理论光谱和SLOAN光谱(使用SSPP测量参数)作为训练数据及测试数据,训练得到两套神经网络系统。
4、使用卡方最小化技术提取恒星大气参数。首先生成两套不同的理论光谱模板,定义观测光谱和理论光谱之间的卡方距离,为了减少计算量,利用半流量点技术来进行初始的温度估计,然后使用剪枝的多项式拟合技术得到最小值,求得有效温度,使用同样的步骤依次求得表面重力值和金属丰度值。第二套模板中使用第一套模板求得得有效温度,不过第二套模板将在以后的应用中计算alpha元素丰度。在本系统中,我们还实现了通过观测的g-r色指数和通过巴尔默(Blamer)线系的强度预测得到有效温度,最终使用了两个理论的有效温度估计和三个经验有效温度估计。
5、利用银河系中的球状星系团和疏散星团的金属丰度值对本系统的参数值准确性进行了评估,并使用其他望远镜观测的高分辨率光谱提取的参数作为真实值,对本系统中的金属丰度参数进行了校正,得到每个算法在不同区间的误差和弥散度,对结果进行了重新加权,获得了较好的准确性。
With the launch of LAMOST Survey, more than 10000 stellar spectra will be getted on each observing night. Spectra contain important informations about celestial bodies. Extraction of the atmospheric physical parameters of stars through the stellar spectra is a basic work in astronomy. The study of stellar spectra of the celestial bodies plays an important role. Numerous methods have been developed in order to extract atmospheric parameter estimates from stellar spectra in a fast, efficient, and automated way. In this study, a stellar atmospheric parameter extraction system is designed, implemented to meet the requiement of LAMOST. The main research work are as follows:
1. Preprocess for the observed spectra of LAMOST. By comparing the observation wavelength to laboratory wavelengths of 11 strong absorption lines, we can calculate the radial velocity. In order to get a good continuum fitting of 3850-9000 A, we first divide the spectrum to two parts:blue (3850-6000A) and red (6000-9000A). The blue and red part are fitted by polynomial separately, and then connected to be fitted by polynomial gain. In this system, the line indices of 83 characteristic lines are calculated for feature extraction.
2. Grid template matching is used to extract stellar atmospheric parameters. Two sets of theoretical spectra template grid are generated using the Kurucz model. One set contains the g-r color index and normalized spectrum of 4400-5500A, the other set contains only the normalized spectrum of 4400-5500A. The distance between observed spectra and the theoretical template spectrum is defined, and Nelder-Mead algorithm is used to search the minimum value. The parameters of the closest theoretical spectrum is believed to be the atmospheric parameters of the observed spectra. Monte Carlo Method is used to simulate the noise distribution, in order to obtain the error of stellar atmospheric parameters.
3. PC A is used to reduce dimensionality of stars spectral data, the Neural Network is used to extract stellar atmospheric parameters. Red and blue part of the spectrum are reduced to 25 dimensions separately.50 dimensions are regarded as the neural network's input, the three atmospheric parameters as output. A three-layer Neural Network is built with10 hidden intermediate nodes. Theoretical spectra and SLOAN spectra (measured by SSPP) are used as training data and two sets of neural network system are obtained.
4. Stellar atmospheric parameters are extracted through the chi-square minimization technique. First of all, two different sets of theoretical spectra templates are generated, and chi-square distance between the observed spectrum and theoretical spectra are defined. Half power point (HPP) is used to estimate the initial temperature to reduce the computation, and then polynomial fitting technology with pruning is used to get the minimum, so the effective temperature is obtained. Surface gravity and metal abundances values are obtained in the same way. The second set of template will use the same temperature as getted by the first set, but the second set of templates will calculate alpha element abundances in the future. In this system, effective temperature predicted from observation g-r color index and Ballmer (Blamer) lines strength are introduced. Two theoretical and three empirical temperatures estimates are obtained finally.
5. Galactic Open and Globular Clusters are used for Validation of metal abundances. The parameters extracted from high-resolution spectra of other telescope are assessed to be true values. These true values of metal abundances are used to correct the result of our system. The offset and dispersion of every algorithm are obtained. The weights are given by their offset and dispersion, and new results are re-weighted to obtain a good accuracy.
引文
1 为了统一和方便使用,Teff是有效温度,g是表面重力(单位是cms-2,[Fe/H]=log10[N(Fe)/N(H2)]-loglo[N(Fe)/N(H2)](?), N代表数量密度
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1. LAMOST大科学工程项目www.lamost.org
2. SLOAN巡天www.sdss.org
3.2DF巡天http://rsaa.anu.edu.au/2dFGRS/
4.中国科学院.LAMOST项目计划建议书[R].1995.
5. 褚耀泉.LAMOST科学观测计划.中国科学技术大学学报.2007.6.
6. 苏洪钧.大天区面积多目标光纤光谱天文望远镜.中国科学院院刊.1999年第五期.
7. Deng L.,Hu J.Y., Xu Y., et al. LAMOST Experiment on Galactic Understanding and Exploration. May 3,2009.
8. Carlos Allende Prieto, et al. a spectroscopy study of the ancient milky way:F-and G-typestars in the third data release of the sloan digital sky survey. The Astrophysical Journal.2006 January 10.
9. Ronald Wllhelm, et al. Spectroscopy of hot stars in the Galatic Halo. Ⅱ. the identification and classification of horizontal-branch and other A-type stars. The Astronomical Journal.1999 Ma.
10. Cenarro A.J., Cardiel N., Gorgas J., et al. Empirical calibration of the near-IR Ca Ⅱ triplet-Ⅰ. The stellar library and index definition. MNRAS,326,959 (2001).
11. Cenarro A.J., Gorgas J., Cardiel N., Pedraz S., Peletier R.F. and Vazdekis A. Empirical calibration of the near-IR Ca Ⅱ triplet-Ⅱ. The atmospheric parameters. MNRAS,326,981 (2001).
12. Re Fiorentin, et al. Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra. Astronomy& Astrophysics,2007,467.
13.张健楠,确定恒星表面有效温度的曲面拟合方法.天文研究与技术,2004,Vol.1.
14.张健楠,确定恒星表面有效温度的非参数估计方法.光谱学与光谱分析,2005 Vol.25.
15. Lee, Young S.;Beers, T. C.;et al. The SDSS-Ⅱ/SEGUE Spectroscopic Parameter :Pipeline.2007 AAS/AAPT Joint Meeting, American Astronomical Society Meeting 209,#168.15.
16. Lee, Young Sun, Beers, Timothy C, et al. The SEGUE Stellar ParameterPipeline. Ⅱ. Validation with Galactic Globular and Open Clusters.2008AJ,136,2050L.
17. Re Fiorentin, P., Bailer-Jones, C. A. L., Lee, Y. S..; et al. Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra.2007A&A,467,1373R.
18. Kev Abazajian, et al. The First Data Release of the Sloan Digital Sky Survey. 2003, AJ,126,2081-2086.
19. Kev Abazajian, et al. The Second Data Release of the Sloan Digital Sky Survey. 2004, AJ,128,502-512.
20. Kev Abazajian, et al.The Third Data Release of the Sloan Digital Sky Survey. 2005, AJ,129,1755-1759.
21. Jennifer Adelman-McCarthy, et al. The Fourth Data Release of the Sloan Digital Sky Survey.2006, ApJS,162,38-48.
22. Jennifer Adelman-McCarthy et al.The Fifth Data Release of the Sloan Digital Sky, Survey.2007, ApJS,172,634.
23. Jennifer Adelman-McCarthy, et al. The Sixth Data Release of the Sloan Digital Sky Survey.2008, ApJS,175,297-313.
24. Kev Abazajian, et al.The Seventh Data Release of the Sloan Digital Sky Survey. 2009, ApJS,182,543-558.
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