LAMOST-DR1中激变变星光谱的数据挖掘研究
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摘要
LAMOST-DR1是郭守敬望远镜正式巡天发布的首批数据,其数量超过目前世界上所有已知恒星巡天项目的光谱总数。这为进一步扩大特殊和稀少天体如激变变星的数量提供了样本,同时也对天文数据处理方法和技术提出了更高的要求。针对LAMOST的数据特点,提出一种能够在海量天体光谱中自动、快速发现激变变星的方法。该方法使用拉普拉斯特征映射对天体光谱进行降维和重构。结果表明不同类别的天体光谱在拉普拉斯空间中能够得到较明显的区分。在使用粒子群算法对神经网络的参数进行优化后,对LAMOST-DR1的全部数据进行了自动识别。实验共发现了7个激变变星,经过证认,其中2个是矮新星,2个是类新星,1个是高度极化的武仙座AM型。这些光谱,补充了现有的激变变星光谱库。本文验证了拉普拉斯特征映射对天体光谱进行特征提取的有效性,为高维光谱进行降维提供了另一途径。在郭守敬望远镜正式发布的数据中寻找激变变星的首次尝试,实验结果表明该自动化的方法鲁棒性好,速度快,准确率高。该方法也可用于其他大型巡天望远镜的海量光谱处理。
LAMOST-DR1 is the first data released by Guoshoujing telescop,which has the largest number of stellar spectra in the world at present The data set provides the data source for searching for special and rare celestial objects like cataclysmic variable stars.Meanwhile,it requires more advanced astronomical data processing methods and techniques.A data mining method for cataclysmic variable spectra in massive spectra data is proposed in this paper.Different types of celestial spectra show obvious difference in the feature space constructed with Laplacian Eigenmap method.The parameters of artificial neural network are optimized with particle swarm optimization method and the total LAMOST-DRl data is processed.7 cataclysmic variable star spectra are found in the experiment including 2 dwarf nova,2 nova like variables and a highly polarized AM Her type.The newly found spectra enrich the current cataclysmic variable spectra library.The experiment is the first attempt of searching for cataclysmic variable star spectra with Guoshoujing telescope data and the results show that our approach is feasible in LAMOST data.This method is also applicable for mining other special celestial objects in sky survey telescope data.
LAMOST-DR1 is the first data released by Guoshoujing telescop,which has the largest number of stellar spectra in the world at present The data set provides the data source for searching for special and rare celestial objects like cataclysmic variable stars.Meanwhile,it requires more advanced astronomical data processing methods and techniques.A data mining method for cataclysmic variable spectra in massive spectra data is proposed in this paper.Different types of celestial spectra show obvious difference in the feature space constructed with Laplacian Eigenmap method.The parameters of artificial neural network are optimized with particle swarm optimization method and the total LAMOST-DRl data is processed.7 cataclysmic variable star spectra are found in the experiment including 2 dwarf nova,2 nova like variables and a highly polarized AM Her type.The newly found spectra enrich the current cataclysmic variable spectra library.The experiment is the first attempt of searching for cataclysmic variable star spectra with Guoshoujing telescope data and the results show that our approach is feasible in LAMOST data.This method is also applicable for mining other special celestial objects in sky survey telescope data.
引文
[1]Zhao Gang,Zhao Yongheng,Chu Yaoquan,et al.Research in Astronomy and Astrophysics,2012,12:723.
[2]Downes R A,Shara M M.Publications of the Astronomical Society of the Pacific,1993,105:127.
[3]Downes R A,Webbink R F,Shara M M,et al.Publications of the Astronomical Society of the Pacific,2001,113:764.
[4]Connolly A J,Szalay A S,Bershady M A,et al.Astron.J.,1995,110(3):1071.
[5]Madgwick D S,Coil A L,et al.The Astrophysical Journal,2003,599:997.
[6]Szkody P,et al.The Astrophysical Journal,2005,129:2386.
[7]Szkody P,et al.The Astrophysical Journal,2006,131:973.
[8]Szkody P,et al.The Astrophysical Journal,2007,134:185.
[9]Szkody P,et al.The Astrophysical Journal,2009,137:4011.
[10]Szkody P,et al.The Astrophysical Journal,2011,142:181.
[11]Belkin M,Niyogi P.Neural Computation,2003,15:1373.
[2]Downes R A,Shara M M.Publications of the Astronomical Society of the Pacific,1993,105:127.
[3]Downes R A,Webbink R F,Shara M M,et al.Publications of the Astronomical Society of the Pacific,2001,113:764.
[4]Connolly A J,Szalay A S,Bershady M A,et al.Astron.J.,1995,110(3):1071.
[5]Madgwick D S,Coil A L,et al.The Astrophysical Journal,2003,599:997.
[6]Szkody P,et al.The Astrophysical Journal,2005,129:2386.
[7]Szkody P,et al.The Astrophysical Journal,2006,131:973.
[8]Szkody P,et al.The Astrophysical Journal,2007,134:185.
[9]Szkody P,et al.The Astrophysical Journal,2009,137:4011.
[10]Szkody P,et al.The Astrophysical Journal,2011,142:181.
[11]Belkin M,Niyogi P.Neural Computation,2003,15:1373.