基于殘差分布度量的LAMOST M矮星細(xì)分類研究
發(fā)布時間：2018-10-17 19:12
【摘要】：隨著天文觀測儀器設(shè)備以及數(shù)據(jù)收集能力的大幅度提高,大量的巡天項(xiàng)目會產(chǎn)生海量的巡天數(shù)據(jù),如我國的LAMOST巡天項(xiàng)目。如何對這些海量的光譜數(shù)據(jù)進(jìn)行有效的處理與分析,是天文學(xué)研究中一個重要的研究內(nèi)容。通過對光譜數(shù)據(jù)進(jìn)行分析處理,可以確定恒星大氣物理參數(shù)和天體的頻譜空間分布等形態(tài)結(jié)構(gòu)信息。恒星光譜分類是天文數(shù)據(jù)處理中一項(xiàng)非常重要的工作,主要應(yīng)用于大量的巡天數(shù)據(jù)搜尋已知類型的天體光譜。通過事先給出有類別的兩類或多類光譜訓(xùn)練一個分類器,對于新的光譜通過該分類器給出一個分類。模板庫可以看作為一個訓(xùn)練好的分類器,將待測光譜與模板庫中的光譜進(jìn)行匹配以獲得分類結(jié)果。本文就是利用殘差分布度量的方法對LAMOST M矮星光譜進(jìn)行細(xì)分類研究。殘差分布度量是一種光譜間的距離度量方法,計算光譜之間的距離時,先將兩條光譜進(jìn)行歸一化處理,之后計算對應(yīng)波長采樣點(diǎn)處的殘差,最終以殘差分布的標(biāo)準(zhǔn)差作為光譜之間的距離。對待測光譜進(jìn)行分類時,首先需要對待測光譜進(jìn)行插值、光譜歸一化等預(yù)處理操作；之后利用殘差分布距離度量方法計算出待測光譜與模板庫中每條模板光譜之間的距離,從中找出距離最小的那一條模板光譜；最終將該模板光譜的類型作為待測光譜的類型。本文分別以模擬光譜和實(shí)測光譜作為實(shí)驗(yàn)數(shù)據(jù)進(jìn)行了分類實(shí)驗(yàn),其中模擬光譜由模板光譜生成,實(shí)測光譜采用的是LAMOST DR2中的M星實(shí)測光譜。實(shí)驗(yàn)結(jié)果表明,殘差分布度量方法能比較準(zhǔn)確地對光譜數(shù)據(jù)進(jìn)行分類,與傳統(tǒng)分類方法相比分類效果更好。光譜的分類效果受信噪比、離群點(diǎn)以及殘差標(biāo)準(zhǔn)化系數(shù)等因素的影響。隨著信噪比的增大,光譜分類效果越來越好�？傮w來說,利用殘差分布度量方法對天體光譜進(jìn)行分類是具有可行性的。本文中的分類方法和處理流程有待于進(jìn)一步的研究,使其達(dá)到一個更好的分類效果,從而使該方法得到更加廣泛地應(yīng)用。
[Abstract]:With the great improvement of astronomical observation instruments and data collection ability, a large number of sky survey projects will produce massive data, such as the LAMOST survey project in China. How to effectively process and analyze these massive spectral data is an important research content in astronomical research. By analyzing and processing the spectral data, the morphological structure information such as the physical parameters of the stellar atmosphere and the spatial distribution of the celestial spectrum can be determined. Star spectral classification is a very important work in astronomical data processing. A classifier is trained by giving two or more kinds of spectra with a class in advance, and a classification of the new spectrum is given by the classifier. The template library can be regarded as a trained classifier. The spectrum to be tested is matched with the spectrum in the template library to obtain the classification results. In this paper, the method of residual distribution metric is used to subdivide the spectrum of LAMOST M dwarf star. The residual distribution measure is a method of measuring the distance between spectra. When calculating the distance between spectra, the two spectra are normalized first, and then the residuals at the sampling points of corresponding wavelength are calculated. Finally, the standard deviation of the residual error distribution is taken as the distance between the spectra. When classifying the measured spectrum, the interpolation and normalization of the measured spectrum are needed first, and then the distance between the measured spectrum and each template spectrum in the template library is calculated by using the residual distribution distance measurement method. Finally, the type of the template spectrum is taken as the type of the spectrum to be measured. In this paper, the simulated spectra and the measured spectra are used as the experimental data, respectively. The simulated spectra are generated from the template spectra, and the measured spectra are the M-star measured spectra in LAMOST DR2. The experimental results show that the residual distribution measurement method can classify the spectral data accurately, and the classification effect is better than the traditional classification method. The effect of spectral classification is affected by SNR, outlier and residual standardization coefficient. With the increase of SNR, the effect of spectral classification is getting better and better. In general, it is feasible to classify celestial spectra by residual distribution measurement. The classification method and processing flow in this paper need to be further studied to achieve a better classification effect, so that the method can be applied more widely.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P144;P111
本文編號：2277682
[Abstract]:With the great improvement of astronomical observation instruments and data collection ability, a large number of sky survey projects will produce massive data, such as the LAMOST survey project in China. How to effectively process and analyze these massive spectral data is an important research content in astronomical research. By analyzing and processing the spectral data, the morphological structure information such as the physical parameters of the stellar atmosphere and the spatial distribution of the celestial spectrum can be determined. Star spectral classification is a very important work in astronomical data processing. A classifier is trained by giving two or more kinds of spectra with a class in advance, and a classification of the new spectrum is given by the classifier. The template library can be regarded as a trained classifier. The spectrum to be tested is matched with the spectrum in the template library to obtain the classification results. In this paper, the method of residual distribution metric is used to subdivide the spectrum of LAMOST M dwarf star. The residual distribution measure is a method of measuring the distance between spectra. When calculating the distance between spectra, the two spectra are normalized first, and then the residuals at the sampling points of corresponding wavelength are calculated. Finally, the standard deviation of the residual error distribution is taken as the distance between the spectra. When classifying the measured spectrum, the interpolation and normalization of the measured spectrum are needed first, and then the distance between the measured spectrum and each template spectrum in the template library is calculated by using the residual distribution distance measurement method. Finally, the type of the template spectrum is taken as the type of the spectrum to be measured. In this paper, the simulated spectra and the measured spectra are used as the experimental data, respectively. The simulated spectra are generated from the template spectra, and the measured spectra are the M-star measured spectra in LAMOST DR2. The experimental results show that the residual distribution measurement method can classify the spectral data accurately, and the classification effect is better than the traditional classification method. The effect of spectral classification is affected by SNR, outlier and residual standardization coefficient. With the increase of SNR, the effect of spectral classification is getting better and better. In general, it is feasible to classify celestial spectra by residual distribution measurement. The classification method and processing flow in this paper need to be further studied to achieve a better classification effect, so that the method can be applied more widely.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P144;P111
【共引文獻(xiàn)】
相關(guān)期刊論文 前7條
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5 盧瑜;李鄉(xiāng)儒;楊坦;;基于LAMOST光譜的恒星大氣物理參數(shù)估計[J];光譜學(xué)與光譜分析;2014年11期
6 石志鑫;Georges Comte;羅阿理;屠良平;趙永恒;吳福朝;;LAMOST類星體[OIII]線附近天光背景扣除[J];光譜學(xué)與光譜分析;2014年11期
7 陳建軍;白仲瑞;李廣偉;王淑清;羅阿理;趙永恒;;光纖轉(zhuǎn)動引起光纖光譜效率變化與改正[J];光譜學(xué)與光譜分析;2015年04期
，本文編號：2277682
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