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

【摘要】：人類關(guān)于恒星本質(zhì)的絕大多數(shù)知識(shí),幾乎都是通過對(duì)恒星光譜的研究而得到的。恒星大氣物理參數(shù),包括恒星的有效溫度、表面重力、化學(xué)豐度,是導(dǎo)致恒星光譜差異的重要因素。目前國(guó)際上有多種通用的恒星大氣物理參數(shù)提取算法,利用中低分辨率光譜以及測(cè)光數(shù)據(jù),在一個(gè)相對(duì)較窄的參數(shù)空間中,提取出相對(duì)準(zhǔn)確的物理參數(shù)。 本文主要研究了基于模板匹配的恒星大氣參量的自動(dòng)測(cè)量方法,采用的模板庫(kù)包括理論模板庫(kù)、實(shí)測(cè)模板庫(kù)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(guān)算法應(yīng)用到恒星大氣物理參數(shù)的自動(dòng)測(cè)量中,通過對(duì)不同的實(shí)測(cè)數(shù)據(jù)的實(shí)驗(yàn)表明了這幾種方法的有效性。另外還通過實(shí)驗(yàn)說明了不同歸一化方法以及光譜的信噪比對(duì)測(cè)量結(jié)果的影響。為降低模板匹配的復(fù)雜度,本文提出了一種利用人工神經(jīng)網(wǎng)絡(luò)(ANN)進(jìn)行粗估溫度縮小匹配模板數(shù)的方法,此外還可以將程序部署到并行計(jì)算環(huán)境中,以進(jìn)一步提高效率最終在Linux環(huán)境下實(shí)現(xiàn)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(shù)自動(dòng)測(cè)量的研究。LAMOST已經(jīng)進(jìn)入先導(dǎo)巡天階段,即將開始正式巡天,會(huì)產(chǎn)生大量光譜,本文的目的是對(duì)一維恒星光譜進(jìn)行處理,利用模板匹配的相關(guān)算法,自動(dòng)獲得恒星大氣物理參數(shù)。本文的工作包括以下幾點(diǎn)： 1、提出了一種利用人工神經(jīng)網(wǎng)絡(luò)(ANN)進(jìn)行粗估溫度縮小匹配模板數(shù)的方法,從而降低模板匹配的復(fù)雜度,提高了模板匹配的效率,大大縮短匹配時(shí)間。 2、重點(diǎn)研究通過模板匹配方法測(cè)量恒星大氣物理參數(shù)的算法,并通過對(duì)不同的實(shí)測(cè)數(shù)據(jù)的實(shí)驗(yàn)表明了幾種模板匹配算法的有效性。 3、通過實(shí)驗(yàn)說明了不同歸化方法以及光譜的信噪比對(duì)測(cè)量結(jié)果的影響。 4、將程序部署到并行計(jì)算環(huán)境中，，以進(jìn)一步提高效率。 5、在Linux環(huán)境下用Python語(yǔ)言結(jié)合SciPy、NumPy、PyFITS及Matplotlib工具包實(shí)現(xiàn)基于模板匹配的恒星大氣物理參數(shù)自動(dòng)測(cè)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(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：P144

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 汪惺惺;LAMOST科學(xué)計(jì)算云平臺(tái)系統(tǒng)的構(gòu)建與應(yīng)用[D];山東大學(xué);2013年

本文編號(hào)：1836277