Yan Taisheng1; Zhang Yanxia1; Zhao Yongheng1; Li Ji2
Source Publicationsciencechinaphysicsmechanicsastronomy
AbstractAutoClass 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.
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yan Taisheng,Zhang Yanxia,Zhao Yongheng,et al. explorationofsdssstellardatabasebyautoclass[J]. sciencechinaphysicsmechanicsastronomy,2011,54(9):1717.
APA Yan Taisheng,Zhang Yanxia,Zhao Yongheng,&Li Ji.(2011).explorationofsdssstellardatabasebyautoclass.sciencechinaphysicsmechanicsastronomy,54(9),1717.
MLA Yan Taisheng,et al."explorationofsdssstellardatabasebyautoclass".sciencechinaphysicsmechanicsastronomy 54.9(2011):1717.
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