Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Net-works

For LAMOST, the largest sky survey program in China, the solution ofthe problem of automatic discrimination of stars from galaxies by spectra has shownthat the results of the PSF test can be significantly refined. However, the problemis made worse when the redshifts of galaxies are not available. We present a newautomatic method of star/(normal) galaxy separation, which is based on StatisticalMixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN).This work is a continuation of our previous one, where active and non-active celestialobjects were successfully segregated. By combining the method in this paper andthe previous one, stars can now be effectively separated from galaxies and AGNs bytheir spectra-a major goal of LAMOST, and an indispensable step in any automaticspectrum classification system. In our work, the training set includes standardstellar spectra from Jacoby's spectrum library and simulated galaxy spectra of E0,SO, Sa, Sb types with redshift ranging from 0 to 1.2, and the test set of stellarspectra from Pickles' atlas and SDSS spectra of normal galaxies with SNR of 13.Experiments show that our SMM-RBFNN is more efficient in both the trainingand testing stages than the BPNN (back propagation neural networks), and moreimportantly, it can achieve a good classification accuracy of 99.22% and 96.52%,respectively for stars and normal galaxies.

作 者： Dong-Mei Qin Ping Guo Zhan-Yi Hu Yong-Heng Zhao   作者單位： Dong-Mei Qin,Zhan-Yi Hu(National Laboratory of Pattern Recognition Laboratory, Institute of Automation, Chinese Academy of Sciences, Beijing 100080)

Ping Guo(Department of Computer Sciences, Beijing Normal University, Beijing 100875)

Yong-Heng Zhao(National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012) 

刊 名： 天體物理學(xué)報(bào)(英文版)  ISTIC SCI 英文刊名： CHINESE JOURNAL OF ASTRONOMY AND ASTROPHYSICS  年，卷(期)： 2003 3(3)  分類號(hào)： P14  關(guān)鍵詞： methods: data analysis-techniques: spectroscopic-stars: gen-eral-galaxies: stellar content  

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