本文選題：郭守敬望遠鏡(LAMOST) + 天體光譜��； 參考：《山東大學》2012年碩士論文

【摘要】：人類關于恒星本質的絕大多數知識,幾乎都是通過對恒星光譜的研究而得到的。恒星大氣物理參數,包括恒星的有效溫度、表面重力、化學豐度,是導致恒星光譜差異的重要因素。目前國際上有多種通用的恒星大氣物理參數提取算法,利用中低分辨率光譜以及測光數據,在一個相對較窄的參數空間中,提取出相對準確的物理參數。 本文主要研究了基于模板匹配的恒星大氣參量的自動測量方法,采用的模板庫包括理論模板庫、實測模板庫兩大類,將模板匹配算法包括K-最鄰近算法、卡方最小化算法、交義相關算法應用到恒星大氣物理參數的自動測量中,通過對不同的實測數據的實驗表明了這幾種方法的有效性。另外還通過實驗說明了不同歸一化方法以及光譜的信噪比對測量結果的影響。為降低模板匹配的復雜度,本文提出了一種利用人工神經網絡(ANN)進行粗估溫度縮小匹配模板數的方法,此外還可以將程序部署到并行計算環(huán)境中,以進一步提高效率最終在Linux環(huán)境下實現程序。 本研究的工作介紹 本文的主要工作是基于模板匹配的恒星大氣物理參數自動測量的研究。LAMOST已經進入先導巡天階段,即將開始正式巡天,會產生大量光譜,本文的目的是對一維恒星光譜進行處理,利用模板匹配的相關算法,自動獲得恒星大氣物理參數。本文的工作包括以下幾點： 1、提出了一種利用人工神經網絡(ANN)進行粗估溫度縮小匹配模板數的方法,從而降低模板匹配的復雜度,提高了模板匹配的效率,大大縮短匹配時間。 2、重點研究通過模板匹配方法測量恒星大氣物理參數的算法,并通過對不同的實測數據的實驗表明了幾種模板匹配算法的有效性。 3、通過實驗說明了不同歸化方法以及光譜的信噪比對測量結果的影響。 4、將程序部署到并行計算環(huán)境中，，以進一步提高效率。 5、在Linux環(huán)境下用Python語言結合SciPy、NumPy、PyFITS及Matplotlib工具包實現基于模板匹配的恒星大氣物理參數自動測量程序
[Abstract]:The vast majority of human knowledge about the nature of stars is almost obtained through the study of stellar spectra. Stellar atmospheric physical parameters, including star effective temperature, surface gravity and chemical abundance, are important factors leading to star spectral differences. At present, there are many universal algorithms for extracting atmospheric physical parameters of stars in the world. Using low and medium resolution spectra and photometry data, relatively accurate physical parameters are extracted in a relatively narrow parameter space. This paper mainly studies the automatic measurement method of stellar atmospheric parameters based on template matching. The template library includes theoretical template library and measured template library. Template matching algorithms include K- nearest neighbor algorithm and chi-square minimization algorithm. The cross-sense correlation algorithm is applied to the automatic measurement of the physical parameters of stellar atmosphere. Experiments on different measured data show the effectiveness of these methods. In addition, the effects of different normalization methods and spectral signal-to-noise ratio on the measurement results are illustrated by experiments. In order to reduce the complexity of template matching, this paper proposes a method of reducing the number of matching templates by using artificial neural network (Ann) to estimate the temperature roughly. In addition, the program can be deployed to parallel computing environment. To further improve the efficiency of the final implementation of the program in the Linux environment. Introduction to the work of this study The main work of this paper is to study the automatic measurement of atmospheric physical parameters of stars based on template matching. LAMOST has entered the stage of leading sky survey, which will produce a large number of spectra soon. The purpose of this paper is to process the spectrum of one-dimensional stars. Using the correlation algorithm of template matching, the atmospheric parameters of stars can be obtained automatically. The work of this paper includes the following points: 1. An artificial neural network (Ann) method is proposed to reduce the number of matching templates, which can reduce the complexity of template matching, improve the efficiency of template matching and greatly shorten the matching time. 2. The algorithm of measuring the physical parameters of stellar atmosphere by template matching method is studied emphatically, and the validity of several template matching algorithms is proved by experiments on different measured data. 3. The effects of different domestication methods and spectral signal-to-noise ratio on the measurement results are illustrated by experiments. In order to further improve efficiency, the program is deployed to parallel computing environment. 5. The automatic measurement program of atmospheric physical parameters of stars based on template matching is realized by using Python language, SciPyNum PyFITS and Matplotlib toolkit in Linux environment.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：P144

【引證文獻】

相關碩士學位論文 前1條

1 汪惺惺;LAMOST科學計算云平臺系統(tǒng)的構建與應用[D];山東大學;2013年

本文編號：1836277