LAMOST一维光谱自动处理

发布时间：2018-04-20 23:10

本文选题：郭守敬望远镜(LAMOST) + 天体光谱　；参考：《山东大学》2011年硕士论文

【摘要】：天文学是一门古老的科学,自有人类文明史以来,天文学就有重要的地位。观测仪器设备及数据收集能力的大幅度提高,使得我们迈入了天文观测数据的“雪崩”时代。天体在光学波段的光谱包含着丰富的物理信息。星系的光谱可以给出它们的距离、构成、分布和运动等信息。恒星的光谱可以确定它们的分布和运动、光度、温度、化学组成等物理状态。从大量天体的光谱观测中还会发现奇异的天体和天文现象,将引起人类对宇宙天体的新认识。 LAMOST巡天正式开始,每晚的观测将要产生数万条光谱。整个巡天计划完成将会产生107数量级的光谱数据,如此庞大数量的光谱显然不能通过传统的人工方式进行的,因此需要研究相关的算法进行光谱的自动处理。在一维光谱处理中,恒星参数测量部分为银河系恒星光谱巡天提供恒星运动学、化学丰度、有效温度、有效重力加速度等信息,是一维光谱处理中非常重要的部分。从海量巡天数据发现特殊稀少未知天体,能够为天文学各种研究提供样本支持。本研究的工作主要分为两部分： (1)设计并实现适用于LAMOST光谱的恒星大气参数测量系统,包括光谱预处理、参数测量等模块,其中参数测量模块支持扩展性,现已集成SSPP、UlySS等软件包,并加入PLS方法,随着研究的不断进行,会有更多的方法集成进来。软件系统采用Python,结合GTK实现图形用户界面,运用多线程编程计算实现对海量光谱的快速批量处理。 (2)研究可用于LAMOST光谱中发现特殊天体的数据挖掘的算法,包括有指导和无指导两类,其中前者主要发现一些已知的特殊天体,而后者主要是发现一些未知的特殊天体。研究的方法主要包括随机森林算法及遗传算法等。
[Abstract]:Astronomy is an ancient science, since the history of human civilization, astronomy has played an important role. With the great improvement of observational equipment and data collection ability, we have entered the "avalanche" era of astronomical observation data. The spectra of celestial bodies in the optical band contain abundant physical information. The spectra of galaxies can give information about their distance, composition, distribution, and motion. The spectra of stars determine their distribution and motion, luminosity, temperature, chemical composition and other physical states. Strange celestial bodies and astronomical phenomena will also be found from the spectral observations of a large number of celestial bodies, which will lead to a new understanding of the celestial bodies in the universe. The LAMOST survey officially begins, and observations each night will produce tens of thousands of spectra. The completion of the whole survey plan will produce 107 order of magnitude spectral data. It is obvious that such a large number of spectra can not be carried out by traditional manual methods, so it is necessary to study related algorithms for spectrum automatic processing. In the one-dimensional spectral processing, the measurement of star parameters provides the information of star kinematics, chemical abundance, effective temperature and effective gravity acceleration for the spectral survey of galactic stars, which is a very important part of one-dimensional spectral processing. Special rare unknown bodies can be found from massive survey data, which can provide sample support for various astronomical studies. The work of this study is divided into two parts: 1) designing and implementing the stellar atmospheric parameter measurement system suitable for LAMOST spectrum, including spectral preprocessing, parameter measurement and other modules, in which the parameter measurement module supports expansibility. The software package SSPPU UlySS has been integrated, and the PLS method has been added to the system. As research continues, more methods will be integrated. The software system uses Python and GTK to realize the graphical user interface. The multithread programming is used to realize the fast batch processing of the mass spectrum. This paper studies the data mining algorithms for discovering special objects in the LAMOST spectrum, including guided and unguided data mining, in which the former mainly finds some known special objects, while the latter mainly finds some unknown special celestial bodies. The research methods mainly include stochastic forest algorithm and genetic algorithm.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TP274;P111

【引证文献】