LAMOST一维光谱自动处理
|
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。
As an ancient science, astronomy played an import part since the beginning of human civilization. With the great improvement of observing equipment and data collection capabilities, astronomical observations come into the avalanche era. The optical spectral of celestial bodies contain a great deal of information. From the spectra of galaxies, we can get their distance, composition, distribution and motion etc. From the stellar spectra,their distribution, motion, luminosity, temperature and its chemical composition etc. can be obtained. Furthermore, we can discover some special, rare and unknown objects or phenomena which will lead to human new understanding of celestial bodies.
With the launch of LAMOST Survey, tens of thousands of spectra of celestial bodies will be collected in each observation night and totally about 10,000,000 spectra will be obtained. It's difficult to deal such massive spectra by hand, hence some algorithms should be developed to process the spectra automatically.
In the LAMOST 1D spectra processing Pipeline, the Stellar Paramater moudle (LAMOST_1DSP) provide physical parameters which are useful in LEGUE.The discovered spectra of special, rare, unknown celestial objects will be large samples for astronomy research.
The work consists of two parts:
(1) The design and implementation of LAMOST automated 1D Stellar atmospheric Parameters measurement system (LAMOST 1DSP), including the spectral preprocessing and parameter measurement modules which support the expansion of parameter measurement. And now Latter module has integrated SSPP, UlySS packages and the PLS method. There will be more methods integrated in the future work. The system is implemented using Python, GTK and multi-threaded programming.
(2) Study some data mining algorithms to discover spectra to find specific objects. The algorithms are divided into two parts:supervised methods and un supervised methods. Research methods include random forest algorithm and genetic algorithm etc.
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。
As an ancient science, astronomy played an import part since the beginning of human civilization. With the great improvement of observing equipment and data collection capabilities, astronomical observations come into the avalanche era. The optical spectral of celestial bodies contain a great deal of information. From the spectra of galaxies, we can get their distance, composition, distribution and motion etc. From the stellar spectra,their distribution, motion, luminosity, temperature and its chemical composition etc. can be obtained. Furthermore, we can discover some special, rare and unknown objects or phenomena which will lead to human new understanding of celestial bodies.
With the launch of LAMOST Survey, tens of thousands of spectra of celestial bodies will be collected in each observation night and totally about 10,000,000 spectra will be obtained. It's difficult to deal such massive spectra by hand, hence some algorithms should be developed to process the spectra automatically.
In the LAMOST 1D spectra processing Pipeline, the Stellar Paramater moudle (LAMOST_1DSP) provide physical parameters which are useful in LEGUE.The discovered spectra of special, rare, unknown celestial objects will be large samples for astronomy research.
The work consists of two parts:
(1) The design and implementation of LAMOST automated 1D Stellar atmospheric Parameters measurement system (LAMOST 1DSP), including the spectral preprocessing and parameter measurement modules which support the expansion of parameter measurement. And now Latter module has integrated SSPP, UlySS packages and the PLS method. There will be more methods integrated in the future work. The system is implemented using Python, GTK and multi-threaded programming.
(2) Study some data mining algorithms to discover spectra to find specific objects. The algorithms are divided into two parts:supervised methods and un supervised methods. Research methods include random forest algorithm and genetic algorithm etc.
引文
[1]顾炎武.日知录[M].明末清初.
[2]赵永恒.互联网时代的天文学革命：虚拟天文台[J].科学(上海),2002,54(002)：13-17.
3] Su D, Cui X, Wang Y,等Large-sky-area multiobject fiber spectroscopic telescope (LAMOST) and its key technology; proceedings of the SPIE, F,1998 [C].
4] Xing X, Hu H, Du H,等A Parallel Controllable Optical Fiber Positioning System For LAMOST [J]. JOURNAL-CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY,1997,27:492-495.
5] Su H, Cui X. LAMOST project and its current status; proceedings of the SPIE, F, 2003 [C].
[6]赵永恒LAMOST与暗能量[J].科学(上海),2009,61(2)
[7]赵永恒.天体光谱获取率最高的望远镜——LAMOST [J]现代物理知识,2007,19(005)：3-5.
[8]褚耀泉LAMOST科学观测计划[J].中国科学技术大学学报,2007,37(006)：591-595.
[9]Cui X. LAMOST Project and its First Flight; proceedings of the SPIE, F,2009 [C].
[10]赵永恒fits文件解析[D],2006.
[11]Greisen Ew, Calabretta Mr. Representations of world coordinates in FITS [J]. Arxiv preprint astro-ph/0207407,2002,
[12]张垒LAMOST二维光谱数据处理方法研究与软件开发[D].合肥；中国科学技术大学,2007.
[13]Ali Luo, Yanxia Zhang, Yongheng Zhao. Design and implementation of the spectra reduction and analysis software for LAMOST; proceedings of the SPIE, F, 2004[C]. Advanced Software, Control, and Communication Systems for Astronomy.
[14]Cui B, Ye Z, Bai Z. Spectral Extraction Algorithm for LAMOST Two-dimensional Fiber-spectroscopic Images [J]. Chinese Astronomy and Astrophysics,2009, 33(1):99-112.
[15]王凤飞LAMOST一维光谱处理与分析软件的设计和实现[D].北京；中国科学院国家天文台,2009.
[16]Lee Ys, Beers Tc, Sivarani T,等. The SEGUE Stellar Parameter Pipeline. Ⅰ. Description and Comparison of Individual Methods [J]. The Astronomical Journal, 2008,136:2022-2049.
[17]Lee Ys, Beers Tc, Sivarani T,等. The SEGUE Stellar Parameter Pipeline. Ⅱ. Validation with Galactic Globular and Open Clusters [J]. The Astronomical Journal,2008,136:2050-2069.
[18]Prieto Ca, Sivarani T, Beers Tc,等. The SEGUE Stellar Parameter Pipeline. Ⅲ. Comparison with High-Resolution Spectroscopy of SDSS/SEGUE Field Stars [J]. The Astronomical Journal,2008,136:2070-2082.
[19]Koleva M, Prugniel P, Bouchard A,等. ULySS:a full spectrum fitting package [J]. Astronomy and Astrophysics,2009,501(3):1269-1279.
[20]Zhao G, Chen Yq, Shi Jr,等. Stellar Abundance and Galactic Chemical Evolution through LAMOST Spectroscopic Survey [J]. Chinese Journal of Astronomy and Astrophysics,2006,6:265-280.
[21]黄润乾.恒星物理[M].北京：科学出版社.1998.
[22]吴悦.恒星光谱基本物理参量自动测量的实现及应用[D].北京；中国科学院国家大文台,2010.
[23]Katz D, Soubiran C, Cayrel R,等On-line determination of stellar atmospheric parameters Teff, log g,[Fe/H] from ELODIE echelle spectra. Ⅰ-The method [J]. Arxiv preprint astro-ph/9806232,1998,
[24]Soubiran C, Odenkirchen M, Le Campion Jf. Fundamental properties of the open cluster NGC 2355 [J]. Arxiv preprint astro-ph/0003148,2000,
[25]Soubiran C, Bienaym O, Siebert A. Vertical distribution of Galactic disk stars I-Kinematics and metallicity [J]. Arxiv preprint astro-ph/0210628,2002,
26] Steinmetz M, Zwitter T, Siebert A,等The radial velocity experiment (RAVE): first data release[J]. The Astronomical Journal,2006,132:1645-1668.
27] Recio-Blanco A, Bijaoui A, De Laverny P. Automated derivation of stellar atmospheric parameters and chemical abundances:the MATISSE algorithm [J]. Mon Not R Astron Soc,2006,370:141-150.
28] Prieto Ca, Beers Tc, Wilhelm R,等A spectroscopic study of the ancient milky way:f-and g-type stars in the third data release of the sloan digital sky survey [J]. The Astrophysical Journal,2006,636:804-820.
29] Shkedy Z, Decin L, Molenberghs G,等Estimating stellar parameters from spectra using a hierarchical Bayesian approach [J]. Monthly Notices of the Royal Astronomical Society,2007,377(1):120-132.
30] Jofre P, Panter B, Hansen Cj,等Stellar atmosphere parameters with MAx, a MAssive compression of x 2 for spectral fitting [J]. Astronomy and Astrophysics, 2010,517:57-74.
[31]Bonifacio P, Caffau E. Automatic abundance analysis of high resolution spectra [J]. Arxiv preprint astro-ph/0212424,2002,
[32]董伟祥LAMOST恒星大气参数提取系统[D].威海；山东大学,2010.
[33]Gulati Rk, Gupta R, Gothoskar P,等Classification of ultraviolet stellar spectra using artificial neural networks [J]. Bulletin of the Astronomical Society of India, 1996,24:21-30.
[34]Fiorentin Pr, Bailer-Jones Cal, Lee Ys,等Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra [J]. Arxiv preprint astro-ph/0703309,2007,
[35]Bailer-Jones Cal, Irwin M, Gilmore G,等Physical parameterization of stellar spectra:the neural network approach [J]. Arxiv preprint astro-ph/9708206,1997,
[36]Bailer-Jones Cal. Stellar parameters from very low resolution spectra and medium band filters:teff, logg and [m/h] using neural networks [J]. Arxiv preprint astro-ph/0003071,2000,
[37]Bailer-Jones Cal. Automated stellar classification for large surveys:a review of methods and results [J]. Arxiv preprint astro-ph/0102223,2001,
[38]Willemsen Pg, Hilker M, Kayser A,等Analysis of medium resolution spectra by automated methods-application to M55 and omega Centauri [J]. Arxiv preprint astro-ph/0503345,2005,
[39]Manteiga M, Ordonez D, Dafonte C,等ANNs and Wavelets:A Strategy for Gaia RVS Low S/N Stellar Spectra Parameterization [J]. Publications of the Astronomical Society of the Pacific,2010,122:608-617.
[40]Snider S, Prieto Ca, Hippel T,等Three-dimensional spectral classification of low-metallicity stars using artificial neural networks [J]. The Astrophysical Journal, 2001,562:528-548.
[41]Giridhar S, Muneer S, Goswami A. Automated classification and stellar parameterization [M].2006.
[42]Luo Al, Wu Y, Zhao J,等Automated stellar spectral analysis software for survey spectra; proceedings of the SPIE, F.2008 [C].
[43]张健楠,吴福朝,罗阿理,等.恒星大气物理参量的非参数估计方法[J].天文学报,2005,46(004)：406-415.
[44]张健楠.恒星光谱大气物理参量自动估计研究[D].北京；中国科学院研究生院2005.
[45]张健楠,吴福朝,罗阿理,等.确定恒星表面有效温度的曲面拟合方法[J].天文研究与技术：国家天文台台刊,2004,1(004)：249-258.
[46]Zhang Jn, Luo A. Automated estimation of stellar fundamental parameters from low resolution spectra:the PLS method [J]. Research in Astronomy and Astrophysics,2009,9(6):712-724.
[47]Li Hn, Zhao G, Christlieb N,等Test observations that search for metal-poor stars with the Guoshoujing Telescope (LAMOST) [J]. Research in Astronomy and Astrophysics,2010,10(8):753-760.
[48]Wu Y, Singh Hp, Prugniel P,等Coude-feed stellar spectral library-atmospheric parameters [J]. Astronomy & Astrophysics,2011,525:71.
19] Yue Wu, Ali Luo, Haining Li,等Automatic Determination of Stellar Atmospheric Parameters and Construction of Stellar Spectral Templates of the Guoshoujing Telescope (LAMOST) [J]. Research in Astronomy and Astrophysics, 2011, accepted
[50]吴悦,罗阿理,施建荣,等.基于郭守敬望远镜(LAMOST)试观测数据新发现的贫金属星候选体[J].天文学进展,2010,28(04)：404-414.
51] Vogt Ss, Allen Sl, Bigelow Be,等HIRES:the high-resolution echelle spectrometer on the Keck 10-m telescope, F,1994[C].
52] Sheinis Ai, Bolte M, Epps Hw,等Esi, a new keck observatory echellette spectrograph and imager [J]. Arxiv preprint astro-ph/0204297,2002,
53] Ramsey Lw, Adams Mt, Barnes Iii Tg,等. Early performance and present status of the Hobby-Eberly Telescope, F,1998 [C].
54] Tull Rg. High-resolution fiber-coupled spectrograph of the Hobby-Eberly Telescope, F,1998 [C].
55] Noguchi K, Ando H, Izumiura H,等. High-dispersion spectrograph (HDS) for Subaru Telescope, F,1998 [C].
[56]张丽华.数据挖掘技术在特殊天体发现中的应用研究[D].威海；山东大学,2009.
57] Tu Lp, Luo Al, Wu Fc,等A method of searching for supernova candidates from massive galaxy spectra [J]. SCIENCE CHINA Physics, Mechanics & Astronomy, 2010,53(10):1928-1938.
58] Tu Lp, Luo Al. New supernova candidates from the SDSS-DR7 spectral survey [J]. Research in Astronomy and Astrophysics,2009,9(6):635-640.
[59]姜斌,罗阿理,赵永恒.海量光谱中激变变星候选体的数据挖掘[J].光谱学与光谱分析,2011,已接收
[60]Szkody P, Henden A, Fraser Oj,等Cataclysmic Variables from Sloan Digital Sky Survey. Ⅳ. The Fourth Year (2003) [J]. The Astronomical Journal,2005, 129:2386-2399.
[61]Szkody P, Henden A, Agueros M,等Cataclysmic Variables from Sloan Digital Sky Survey. V. The Fifth Year (2004) [J]. The Astronomical Journal,2006, 131:973-983.
[62]Szkody P, Henden A, Mannikko L,等Cataclysmic Variables from Sloan Digital Sky Survey. VI. The Sixth Year (2005) [J]. The Astronomical Journal,2007, 134:185-194.
[63]Szkody P, Anderson Sf, Agueros M,等Cataclysmic Variables from The Sloan Digital Sky Survey. Ⅰ. The First Results [J]. The Astronomical Journal,2002, 123:430-442.
[64]Szkody P, Fraser O, Silvestri N,等Cataclysmic Variables from the Sloan Digital Sky Survey. Ⅱ. The Second Year [J]. The Astronomical Journal,2003, 126:1499-1514.
[65]Szkody P, Henden A, Fraser O,等Cataclysmic Variables from the Sloan Digital Sky Survey. Ⅲ. The Third Year [J]. The Astronomical Journal,2004, 128:1882-1893.
[66]徐义明.天文光谱分类算法在分布式环境下的应用研究[D].威海；山东大学,2008.
[67]Liu B, Hsu W, Ma Y. Integrating classification and association rule mining [J]. Knowledge discovery and data mining,1998:80-86.
[68]Horton P, Nakai K. Better prediction of protein cellular localization sites with the k nearest neighbors classifier; proceedings of the ISMB, F,1997 [C].
[69]Breiman L. Classification and regression trees [M]. Chapman & Hall/CRC,1984.
[70]Lewis D. Naive (Bayes) at forty:The independence assumption in information retrieval [J]. Machine Learning:ECML-98,1998:4-15.
[71]Pawlak Z. Rough set approach to knowledge-based decision support [J]. European journal of operational research,1997,99(1):48-57.
[72]Hagan Mt, Demuth Hb, Beale Mh. Neural network design[M]. PWS Boston, MA, 1996.
73] Furey Ts, Cristianini N, Duffy N,等Support vector machine classification and validation of cancer tissue samples using microarray expression data [J]. Bioinformatics,2000,16(10):906-914.
[74]沈学华,周志华Boosting和Bagging综述[J].计算机工程与应用,2000,36(012)：31-32.
75]董乐红,耿国华,高原Boosting算法综述[J].计算机应用与软件,2006,23(008)：27-29.
76] Goldberg De. Genetic algorithm [M]. Addison-Wesley, Reading.1989.
77] Jingchang Pan, Peng Wei, Qiang Guo,等. GA-Optimized Random Forest Classification for High Dimensional Data [J]. ICIC Expresss Letters,2011, 5(5):1529-1534.
78] Site of Leo Breiman http://stat-www.berkeley.edu/users/breiman/RandomForests /cc_home.htm [M].
79] Liaw A, Wiener M. Classification and Regression by randomForest [J]. R news, 2002,2(3):18-22.
80] Prinzie A, Van Den Poel D. Random multiclass classification:Generalizing random forests to random mnl and random nb [J]. Lecture Notes in Computer Science,2007,4653:349-358.
81] Prinzie A, Van Den Poel D. Random forests for multiclass classification:Random multinomial logit[J]. Expert systems with Applications,2008,34(3):1721-1732.
82] Gao D, Zhang Yx, Zhao Yh. Random forest algorithm for classification of multiwavelength data [J]. Research in Astronomy and Astrophysics,2009, 9(2):220-226.
83] Liang-Ping Tu, A-Li Luo, Fu-Chao Wu,等Reducing the Searching Range of Supernova Candidates Automatically in a Flood of Spectra of Galaxies [J].
[84]李宗云,刘武.20个激变变星或相关天体的分光证认[J].天文学报,1999,40(001)：1-15.
[85]姜斌,潘景昌,郭强,等.PCA和相融性度量在聚类算法中的应用[J].电子科技大学学报,2007,36(006)：1292-1295.
[86]姜斌,潘景昌,郭强,等.基于二维主分量分析的光谱数据降维方法[J].现代电子技术,2007,30(014)：21-23.
[87]Pan Jingchang, Wei Peng, Zhang Caiming,等Outlier Detection Of Celestial Spectra Based On Modified GA [J]. ICIC Express Letters,2011,5(1):35-40.
[88]Aggarwal C, Yu S. An effective and efficient algorithm for high-dimensional outlier detection [J]. The VLDB Journal The International Journal on Very Large Data Bases,2005,14(2):211-221.
[89]张继福,蒋义勇,胡立华,等.基于概念格的天体光谱离群数据识别方法[J].自动化学报,2008,34(9)：1060-1066.
[90]施冬冬.贾瑞玉,黄义堂.基于遗传算法的高维离群点检测算法的改进[J].计算机技术与发展,2009,19(003)：141-143.
[2]赵永恒.互联网时代的天文学革命：虚拟天文台[J].科学(上海),2002,54(002)：13-17.
3] Su D, Cui X, Wang Y,等Large-sky-area multiobject fiber spectroscopic telescope (LAMOST) and its key technology; proceedings of the SPIE, F,1998 [C].
4] Xing X, Hu H, Du H,等A Parallel Controllable Optical Fiber Positioning System For LAMOST [J]. JOURNAL-CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY,1997,27:492-495.
5] Su H, Cui X. LAMOST project and its current status; proceedings of the SPIE, F, 2003 [C].
[6]赵永恒LAMOST与暗能量[J].科学(上海),2009,61(2)
[7]赵永恒.天体光谱获取率最高的望远镜——LAMOST [J]现代物理知识,2007,19(005)：3-5.
[8]褚耀泉LAMOST科学观测计划[J].中国科学技术大学学报,2007,37(006)：591-595.
[9]Cui X. LAMOST Project and its First Flight; proceedings of the SPIE, F,2009 [C].
[10]赵永恒fits文件解析[D],2006.
[11]Greisen Ew, Calabretta Mr. Representations of world coordinates in FITS [J]. Arxiv preprint astro-ph/0207407,2002,
[12]张垒LAMOST二维光谱数据处理方法研究与软件开发[D].合肥；中国科学技术大学,2007.
[13]Ali Luo, Yanxia Zhang, Yongheng Zhao. Design and implementation of the spectra reduction and analysis software for LAMOST; proceedings of the SPIE, F, 2004[C]. Advanced Software, Control, and Communication Systems for Astronomy.
[14]Cui B, Ye Z, Bai Z. Spectral Extraction Algorithm for LAMOST Two-dimensional Fiber-spectroscopic Images [J]. Chinese Astronomy and Astrophysics,2009, 33(1):99-112.
[15]王凤飞LAMOST一维光谱处理与分析软件的设计和实现[D].北京；中国科学院国家天文台,2009.
[16]Lee Ys, Beers Tc, Sivarani T,等. The SEGUE Stellar Parameter Pipeline. Ⅰ. Description and Comparison of Individual Methods [J]. The Astronomical Journal, 2008,136:2022-2049.
[17]Lee Ys, Beers Tc, Sivarani T,等. The SEGUE Stellar Parameter Pipeline. Ⅱ. Validation with Galactic Globular and Open Clusters [J]. The Astronomical Journal,2008,136:2050-2069.
[18]Prieto Ca, Sivarani T, Beers Tc,等. The SEGUE Stellar Parameter Pipeline. Ⅲ. Comparison with High-Resolution Spectroscopy of SDSS/SEGUE Field Stars [J]. The Astronomical Journal,2008,136:2070-2082.
[19]Koleva M, Prugniel P, Bouchard A,等. ULySS:a full spectrum fitting package [J]. Astronomy and Astrophysics,2009,501(3):1269-1279.
[20]Zhao G, Chen Yq, Shi Jr,等. Stellar Abundance and Galactic Chemical Evolution through LAMOST Spectroscopic Survey [J]. Chinese Journal of Astronomy and Astrophysics,2006,6:265-280.
[21]黄润乾.恒星物理[M].北京：科学出版社.1998.
[22]吴悦.恒星光谱基本物理参量自动测量的实现及应用[D].北京；中国科学院国家大文台,2010.
[23]Katz D, Soubiran C, Cayrel R,等On-line determination of stellar atmospheric parameters Teff, log g,[Fe/H] from ELODIE echelle spectra. Ⅰ-The method [J]. Arxiv preprint astro-ph/9806232,1998,
[24]Soubiran C, Odenkirchen M, Le Campion Jf. Fundamental properties of the open cluster NGC 2355 [J]. Arxiv preprint astro-ph/0003148,2000,
[25]Soubiran C, Bienaym O, Siebert A. Vertical distribution of Galactic disk stars I-Kinematics and metallicity [J]. Arxiv preprint astro-ph/0210628,2002,
26] Steinmetz M, Zwitter T, Siebert A,等The radial velocity experiment (RAVE): first data release[J]. The Astronomical Journal,2006,132:1645-1668.
27] Recio-Blanco A, Bijaoui A, De Laverny P. Automated derivation of stellar atmospheric parameters and chemical abundances:the MATISSE algorithm [J]. Mon Not R Astron Soc,2006,370:141-150.
28] Prieto Ca, Beers Tc, Wilhelm R,等A spectroscopic study of the ancient milky way:f-and g-type stars in the third data release of the sloan digital sky survey [J]. The Astrophysical Journal,2006,636:804-820.
29] Shkedy Z, Decin L, Molenberghs G,等Estimating stellar parameters from spectra using a hierarchical Bayesian approach [J]. Monthly Notices of the Royal Astronomical Society,2007,377(1):120-132.
30] Jofre P, Panter B, Hansen Cj,等Stellar atmosphere parameters with MAx, a MAssive compression of x 2 for spectral fitting [J]. Astronomy and Astrophysics, 2010,517:57-74.
[31]Bonifacio P, Caffau E. Automatic abundance analysis of high resolution spectra [J]. Arxiv preprint astro-ph/0212424,2002,
[32]董伟祥LAMOST恒星大气参数提取系统[D].威海；山东大学,2010.
[33]Gulati Rk, Gupta R, Gothoskar P,等Classification of ultraviolet stellar spectra using artificial neural networks [J]. Bulletin of the Astronomical Society of India, 1996,24:21-30.
[34]Fiorentin Pr, Bailer-Jones Cal, Lee Ys,等Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra [J]. Arxiv preprint astro-ph/0703309,2007,
[35]Bailer-Jones Cal, Irwin M, Gilmore G,等Physical parameterization of stellar spectra:the neural network approach [J]. Arxiv preprint astro-ph/9708206,1997,
[36]Bailer-Jones Cal. Stellar parameters from very low resolution spectra and medium band filters:teff, logg and [m/h] using neural networks [J]. Arxiv preprint astro-ph/0003071,2000,
[37]Bailer-Jones Cal. Automated stellar classification for large surveys:a review of methods and results [J]. Arxiv preprint astro-ph/0102223,2001,
[38]Willemsen Pg, Hilker M, Kayser A,等Analysis of medium resolution spectra by automated methods-application to M55 and omega Centauri [J]. Arxiv preprint astro-ph/0503345,2005,
[39]Manteiga M, Ordonez D, Dafonte C,等ANNs and Wavelets:A Strategy for Gaia RVS Low S/N Stellar Spectra Parameterization [J]. Publications of the Astronomical Society of the Pacific,2010,122:608-617.
[40]Snider S, Prieto Ca, Hippel T,等Three-dimensional spectral classification of low-metallicity stars using artificial neural networks [J]. The Astrophysical Journal, 2001,562:528-548.
[41]Giridhar S, Muneer S, Goswami A. Automated classification and stellar parameterization [M].2006.
[42]Luo Al, Wu Y, Zhao J,等Automated stellar spectral analysis software for survey spectra; proceedings of the SPIE, F.2008 [C].
[43]张健楠,吴福朝,罗阿理,等.恒星大气物理参量的非参数估计方法[J].天文学报,2005,46(004)：406-415.
[44]张健楠.恒星光谱大气物理参量自动估计研究[D].北京；中国科学院研究生院2005.
[45]张健楠,吴福朝,罗阿理,等.确定恒星表面有效温度的曲面拟合方法[J].天文研究与技术：国家天文台台刊,2004,1(004)：249-258.
[46]Zhang Jn, Luo A. Automated estimation of stellar fundamental parameters from low resolution spectra:the PLS method [J]. Research in Astronomy and Astrophysics,2009,9(6):712-724.
[47]Li Hn, Zhao G, Christlieb N,等Test observations that search for metal-poor stars with the Guoshoujing Telescope (LAMOST) [J]. Research in Astronomy and Astrophysics,2010,10(8):753-760.
[48]Wu Y, Singh Hp, Prugniel P,等Coude-feed stellar spectral library-atmospheric parameters [J]. Astronomy & Astrophysics,2011,525:71.
19] Yue Wu, Ali Luo, Haining Li,等Automatic Determination of Stellar Atmospheric Parameters and Construction of Stellar Spectral Templates of the Guoshoujing Telescope (LAMOST) [J]. Research in Astronomy and Astrophysics, 2011, accepted
[50]吴悦,罗阿理,施建荣,等.基于郭守敬望远镜(LAMOST)试观测数据新发现的贫金属星候选体[J].天文学进展,2010,28(04)：404-414.
51] Vogt Ss, Allen Sl, Bigelow Be,等HIRES:the high-resolution echelle spectrometer on the Keck 10-m telescope, F,1994[C].
52] Sheinis Ai, Bolte M, Epps Hw,等Esi, a new keck observatory echellette spectrograph and imager [J]. Arxiv preprint astro-ph/0204297,2002,
53] Ramsey Lw, Adams Mt, Barnes Iii Tg,等. Early performance and present status of the Hobby-Eberly Telescope, F,1998 [C].
54] Tull Rg. High-resolution fiber-coupled spectrograph of the Hobby-Eberly Telescope, F,1998 [C].
55] Noguchi K, Ando H, Izumiura H,等. High-dispersion spectrograph (HDS) for Subaru Telescope, F,1998 [C].
[56]张丽华.数据挖掘技术在特殊天体发现中的应用研究[D].威海；山东大学,2009.
57] Tu Lp, Luo Al, Wu Fc,等A method of searching for supernova candidates from massive galaxy spectra [J]. SCIENCE CHINA Physics, Mechanics & Astronomy, 2010,53(10):1928-1938.
58] Tu Lp, Luo Al. New supernova candidates from the SDSS-DR7 spectral survey [J]. Research in Astronomy and Astrophysics,2009,9(6):635-640.
[59]姜斌,罗阿理,赵永恒.海量光谱中激变变星候选体的数据挖掘[J].光谱学与光谱分析,2011,已接收
[60]Szkody P, Henden A, Fraser Oj,等Cataclysmic Variables from Sloan Digital Sky Survey. Ⅳ. The Fourth Year (2003) [J]. The Astronomical Journal,2005, 129:2386-2399.
[61]Szkody P, Henden A, Agueros M,等Cataclysmic Variables from Sloan Digital Sky Survey. V. The Fifth Year (2004) [J]. The Astronomical Journal,2006, 131:973-983.
[62]Szkody P, Henden A, Mannikko L,等Cataclysmic Variables from Sloan Digital Sky Survey. VI. The Sixth Year (2005) [J]. The Astronomical Journal,2007, 134:185-194.
[63]Szkody P, Anderson Sf, Agueros M,等Cataclysmic Variables from The Sloan Digital Sky Survey. Ⅰ. The First Results [J]. The Astronomical Journal,2002, 123:430-442.
[64]Szkody P, Fraser O, Silvestri N,等Cataclysmic Variables from the Sloan Digital Sky Survey. Ⅱ. The Second Year [J]. The Astronomical Journal,2003, 126:1499-1514.
[65]Szkody P, Henden A, Fraser O,等Cataclysmic Variables from the Sloan Digital Sky Survey. Ⅲ. The Third Year [J]. The Astronomical Journal,2004, 128:1882-1893.
[66]徐义明.天文光谱分类算法在分布式环境下的应用研究[D].威海；山东大学,2008.
[67]Liu B, Hsu W, Ma Y. Integrating classification and association rule mining [J]. Knowledge discovery and data mining,1998:80-86.
[68]Horton P, Nakai K. Better prediction of protein cellular localization sites with the k nearest neighbors classifier; proceedings of the ISMB, F,1997 [C].
[69]Breiman L. Classification and regression trees [M]. Chapman & Hall/CRC,1984.
[70]Lewis D. Naive (Bayes) at forty:The independence assumption in information retrieval [J]. Machine Learning:ECML-98,1998:4-15.
[71]Pawlak Z. Rough set approach to knowledge-based decision support [J]. European journal of operational research,1997,99(1):48-57.
[72]Hagan Mt, Demuth Hb, Beale Mh. Neural network design[M]. PWS Boston, MA, 1996.
73] Furey Ts, Cristianini N, Duffy N,等Support vector machine classification and validation of cancer tissue samples using microarray expression data [J]. Bioinformatics,2000,16(10):906-914.
[74]沈学华,周志华Boosting和Bagging综述[J].计算机工程与应用,2000,36(012)：31-32.
75]董乐红,耿国华,高原Boosting算法综述[J].计算机应用与软件,2006,23(008)：27-29.
76] Goldberg De. Genetic algorithm [M]. Addison-Wesley, Reading.1989.
77] Jingchang Pan, Peng Wei, Qiang Guo,等. GA-Optimized Random Forest Classification for High Dimensional Data [J]. ICIC Expresss Letters,2011, 5(5):1529-1534.
78] Site of Leo Breiman http://stat-www.berkeley.edu/users/breiman/RandomForests /cc_home.htm [M].
79] Liaw A, Wiener M. Classification and Regression by randomForest [J]. R news, 2002,2(3):18-22.
80] Prinzie A, Van Den Poel D. Random multiclass classification:Generalizing random forests to random mnl and random nb [J]. Lecture Notes in Computer Science,2007,4653:349-358.
81] Prinzie A, Van Den Poel D. Random forests for multiclass classification:Random multinomial logit[J]. Expert systems with Applications,2008,34(3):1721-1732.
82] Gao D, Zhang Yx, Zhao Yh. Random forest algorithm for classification of multiwavelength data [J]. Research in Astronomy and Astrophysics,2009, 9(2):220-226.
83] Liang-Ping Tu, A-Li Luo, Fu-Chao Wu,等Reducing the Searching Range of Supernova Candidates Automatically in a Flood of Spectra of Galaxies [J].
[84]李宗云,刘武.20个激变变星或相关天体的分光证认[J].天文学报,1999,40(001)：1-15.
[85]姜斌,潘景昌,郭强,等.PCA和相融性度量在聚类算法中的应用[J].电子科技大学学报,2007,36(006)：1292-1295.
[86]姜斌,潘景昌,郭强,等.基于二维主分量分析的光谱数据降维方法[J].现代电子技术,2007,30(014)：21-23.
[87]Pan Jingchang, Wei Peng, Zhang Caiming,等Outlier Detection Of Celestial Spectra Based On Modified GA [J]. ICIC Express Letters,2011,5(1):35-40.
[88]Aggarwal C, Yu S. An effective and efficient algorithm for high-dimensional outlier detection [J]. The VLDB Journal The International Journal on Very Large Data Bases,2005,14(2):211-221.
[89]张继福,蒋义勇,胡立华,等.基于概念格的天体光谱离群数据识别方法[J].自动化学报,2008,34(9)：1060-1066.
[90]施冬冬.贾瑞玉,黄义堂.基于遗传算法的高维离群点检测算法的改进[J].计算机技术与发展,2009,19(003)：141-143.