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Photometric identification of blue horizontal branch stars
Smith, K. W.1; Bailer-Jones, C. A. L.1; Klement, R. J.1; Xue, X. X.2
2010-11-01
Source PublicationASTRONOMY & ASTROPHYSICS
Volume522
AbstractWe investigate the performance of some common machine learning techniques in identifying blue horizontal branch (BHB) stars from photometric data. To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from Sloan digital sky survey (SDSS) data. We investigate the performance of three different techniques, namely k nearest neighbour classification, kernel density estimation for discriminant analysis and a support vector machine (SVM). We discuss the performance of the methods in terms of both completeness (what fraction of input BHB stars are successfully returned as BHB stars) and contamination (what fraction of contaminating sources end up in the output BHB sample). We discuss the prospect of trading off these values, achieving lower contamination at the expense of lower completeness, by adjusting probability thresholds for the classification. We also discuss the role of prior probabilities in the classification performance, and we assess via simulations the reliability of the dataset used for training. Overall it seems that no-prior gives the best completeness, but adopting a prior lowers the contamination. We find that the support vector machine generally delivers the lowest contamination for a given level of completeness, and so is our method of choice. Finally, we classify a large sample of SDSS Data Release 7 (DR7) photometry using the SVM trained on the spectroscopic sample. We identify 27 074 probable BHB stars out of a sample of 294 652 stars. We derive photometric parallaxes and demonstrate that our results are reasonable by comparing to known distances for a selection of globular clusters. We attach our classifications, including probabilities, as an electronic table, so that they can be used either directly as a BHB star catalogue, or as priors to a spectroscopic or other classification method. We also provide our final models so that they can be directly applied to new data.
KeywordMethods: Statistical Stars: Horizontal-branch Galaxy: Structure
DOI10.1051/0004-6361/201014381
Indexed BySCI
Language英语
WOS IDWOS:000284153100094
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/7697
Collection光学天文研究部
Affiliation1.Max Planck Inst Astron, D-69117 Heidelberg, Germany
2.CAS, Natl Astron Observ, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Smith, K. W.,Bailer-Jones, C. A. L.,Klement, R. J.,et al. Photometric identification of blue horizontal branch stars[J]. ASTRONOMY & ASTROPHYSICS,2010,522.
APA Smith, K. W.,Bailer-Jones, C. A. L.,Klement, R. J.,&Xue, X. X..(2010).Photometric identification of blue horizontal branch stars.ASTRONOMY & ASTROPHYSICS,522.
MLA Smith, K. W.,et al."Photometric identification of blue horizontal branch stars".ASTRONOMY & ASTROPHYSICS 522(2010).
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