基于线指数的恒星大气物理参数提取方法的研究
发布时间：2018-08-24 12:10
【摘要】：现在国际上的大口径兼大视场望远镜有美国的Sloan数字巡天望远镜,英澳天文台的2dF巡天望远镜,我国的LAMOST巡天望远镜等。它们将得到海量的光谱数据。通过观测获得恒星的光谱,不仅能够确定其大气参数和空间分布,还能够结合年龄和运动学信息,得到银河系不同星族的大气参数,从而为银河系的形成、结构和演化模型提供准确的约束条件。将恒星演化模型与观测结果进行对比,还能有效地追踪银河系自形成以来的演化历史、理解核合成理论,并且对现有的宇宙模型作出检验,促使人们对宇宙演化有更新的认识。恒星大气参数分析是探索恒星、银河系、甚至宇宙演化的一种基本途径。如此海量的光谱数据对光谱的快速有效处理提出了更高的要求。 恒星大气物理参数(有效温度、表面重力、化学丰度)是导致恒星光谱差异的主要因素。恒星大气物理参数的自动测量是LAMOST等大规模巡天望远镜所产生的海量天体光谱数据自动处理中一个重要研究内容。 Lick指数是一个相对较宽的光谱特性,以每个线指数最突出的吸收线命名。这种指数可以忽略流量的校正和红移的错误并对具有更高的信噪比S/N,这让Lick线指数成为了测量大气物理参数的一种理想方法。 本课题利用Lick线指数,根据光谱的海量特点,分别通过线性回归、人工神经网络、支持向量机的方法来设计大气物理参数测量的高效算法。该方法首先对Kurucz训练数据进行筛选,然后利用三种算法对其进行训练,找到最佳模型。最后与DR8测试数据的相应参数进行对比测试。结果表明,该方法能有效准确地从低分辨率光谱中测定恒星大气物理参数。实验结果证明,与目前的方法相比,该方法能够有效地估计恒星大气参数且速度更快,准确度更高。 总的来说,利用Lick线指数来进行大气物理参数的预测是具有可行性的。对于本文中的三种算法模型有待于进行进一步的研究,使其达到一个更好的拟合效果,从而可以将该算法更好的应用于像LAMOST一样的巡天项目中去。
[Abstract]:At present, the international large caliber and large field of view telescopes include the Sloan Digital Sky Survey Telescope of the United States, the 2dF Survey Telescope of the British and Australian Astronomical Observatory, the LAMOST Survey Telescope of China and so on. They will get a great deal of spectral data. The spectra of stars can not only determine the atmospheric parameters and spatial distribution, but also combine the age and kinematics information to obtain the atmospheric parameters of different star families in the Milky way, thus contributing to the formation of the Milky way. The structure and evolution model provide accurate constraints. By comparing the stellar evolution model with the observational results, we can effectively trace the evolution history of the Milky way since its formation, understand the nuclear synthesis theory, and test the existing cosmic models, thus promoting a new understanding of the evolution of the universe. Stellar atmospheric parameter analysis is a basic way to explore the evolution of stars, galaxies, and even the universe. Such a large amount of spectral data put forward a higher demand for fast and effective processing of spectrum. Stellar atmospheric physical parameters (effective temperature, surface gravity, chemical abundance) are the main factors leading to star spectral differences. The automatic measurement of stellar atmospheric physical parameters is an important research content in the automatic processing of massive celestial body spectral data produced by LAMOST and other large-scale survey telescopes. The Lick index is a relatively wide spectral characteristic. Named after the absorption line that is most prominent in each line index. This index can ignore the error of flow correction and redshift and has a higher SNR S / N, which makes the Lick line index an ideal method for measuring the physical parameters of the atmosphere. In this paper, Lick line exponent is used to design an efficient algorithm for measuring atmospheric physical parameters by means of linear regression, artificial neural network and support vector machine, according to the magnanimity of spectrum. Firstly, the Kurucz training data are filtered, then three algorithms are used to train it to find the best model. Finally, it is compared with the corresponding parameters of DR8 test data. The results show that this method can effectively and accurately measure the atmospheric physical parameters of stars from low resolution spectra. The experimental results show that the proposed method can effectively estimate the atmospheric parameters of stars faster and more accurately than the present method. In general, it is feasible to use Lick line index to predict atmospheric physical parameters. The three algorithms in this paper need to be further studied in order to achieve a better fitting effect, so that the algorithm can be better applied to the survey projects like LAMOST.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：P144;TP18
本文编号：2200781
[Abstract]:At present, the international large caliber and large field of view telescopes include the Sloan Digital Sky Survey Telescope of the United States, the 2dF Survey Telescope of the British and Australian Astronomical Observatory, the LAMOST Survey Telescope of China and so on. They will get a great deal of spectral data. The spectra of stars can not only determine the atmospheric parameters and spatial distribution, but also combine the age and kinematics information to obtain the atmospheric parameters of different star families in the Milky way, thus contributing to the formation of the Milky way. The structure and evolution model provide accurate constraints. By comparing the stellar evolution model with the observational results, we can effectively trace the evolution history of the Milky way since its formation, understand the nuclear synthesis theory, and test the existing cosmic models, thus promoting a new understanding of the evolution of the universe. Stellar atmospheric parameter analysis is a basic way to explore the evolution of stars, galaxies, and even the universe. Such a large amount of spectral data put forward a higher demand for fast and effective processing of spectrum. Stellar atmospheric physical parameters (effective temperature, surface gravity, chemical abundance) are the main factors leading to star spectral differences. The automatic measurement of stellar atmospheric physical parameters is an important research content in the automatic processing of massive celestial body spectral data produced by LAMOST and other large-scale survey telescopes. The Lick index is a relatively wide spectral characteristic. Named after the absorption line that is most prominent in each line index. This index can ignore the error of flow correction and redshift and has a higher SNR S / N, which makes the Lick line index an ideal method for measuring the physical parameters of the atmosphere. In this paper, Lick line exponent is used to design an efficient algorithm for measuring atmospheric physical parameters by means of linear regression, artificial neural network and support vector machine, according to the magnanimity of spectrum. Firstly, the Kurucz training data are filtered, then three algorithms are used to train it to find the best model. Finally, it is compared with the corresponding parameters of DR8 test data. The results show that this method can effectively and accurately measure the atmospheric physical parameters of stars from low resolution spectra. The experimental results show that the proposed method can effectively estimate the atmospheric parameters of stars faster and more accurately than the present method. In general, it is feasible to use Lick line index to predict atmospheric physical parameters. The three algorithms in this paper need to be further studied in order to achieve a better fitting effect, so that the algorithm can be better applied to the survey projects like LAMOST.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：P144;TP18
【参考文献】
相关期刊论文 前3条
1 Philippe Prugniel;;Automatic determination of stellar atmospheric parameters and construction of stellar spectral templates of the Guoshoujing Telescope (LAMOST)[J];Research in Astronomy and Astrophysics;2011年08期
2 ;The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)[J];Research in Astronomy and Astrophysics;2012年09期
3 ;Data release of the LAMOST pilot survey[J];Research in Astronomy and Astrophysics;2012年09期
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