本文選題：data　切入點(diǎn)：analysis　出處：《Science China(Physics,Mechanics & Astronomy)》2011年09期 　論文類(lèi)型：期刊論文

【摘要】：AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as the input of AutoClass. Through clustering analysis of this sample by AutoClass, 617 non-stellar candidates are found. These candidates are identified by NED and SIMBAD databases. Most of the identified sources (13 from SIMBAD and 28 from NED respectively) are extragalactic sources (e.g., galaxies, HII, radio sources, infrared sources), some are peculiar stars (e.g., supernovas), and very few are normal stars. The extragalactic sources and peculiar stars of the identified objects occupy 94.1%. The result indicates that this method is an effective and robust clustering algorithm to find non-stellar objects and peculiar stars from the total stellar sample.
[Abstract]:AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as the input of AutoClass. Through clustering analysis of this sample by AutoClass, 617 non-stellar candidates are found. These candidates are identified by NED and SIMBAD databases. Most of the identified sources (13 from SIMBAD and 28 from NED respectively) are extragalactic sources (e.g., galaxies, HII, radio sources, infrared sources), some are peculiar stars (e.g., supernovas), and very few are normal stars. The extragalactic sources and peculiar stars of the identified objects occupy 94.1%. The result indicates that this method is an effective and robust clustering algorithm to find non-stellar objects and peculiar stars from the total stellar sample.
【基金】：supported by the National Natural Science Foundation of China (Grant Nos. 10778724 and 11033001) the Natural Science Foundation of Education Department of Hebei Province (GrantNo. ZD2010127) the Young Researcher Grant of National Astronomical Observatories, Chinese Academy of Sciences
【分類(lèi)號(hào)】：P144

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 ;k-Nearest Neighbors for automated classification of celestial objects[J];Science in China(Series G:Physics,Mechanics & Astronomy);2008年07期

2 嚴(yán)太生;張彥霞;趙永恒;李冀;;基于自動(dòng)聚類(lèi)算法(AutoClass)的恒星/星系分類(lèi)[J];中國(guó)科學(xué)(G輯:物理學(xué) 力學(xué) 天文學(xué));2009年12期

【共引文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 孫曉博;基于粗糙集理論的聚類(lèi)算法研究[D];湖南農(nóng)業(yè)大學(xué);2011年

2 徐長(zhǎng)龍;藥品集中招標(biāo)采購(gòu)中標(biāo)段劃分問(wèn)題研究[D];哈爾濱工業(yè)大學(xué);2010年

【二級(jí)參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 張蕾,何小榮,陳丙珍;常減壓裝置生產(chǎn)數(shù)據(jù)的聚類(lèi)分析[J];計(jì)算機(jī)與應(yīng)用化學(xué);2003年Z1期

2 包雷,李澤,孫之榮;貝葉斯聚類(lèi)在基因表達(dá)譜知識(shí)挖掘中的應(yīng)用[J];生物物理學(xué)報(bào);2002年01期

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本文編號(hào)：1588537