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          Institute: MPI für Astronomie     Collection: Publikationen_mpia     Display Documents



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ID: 559475.0, MPI für Astronomie / Publikationen_mpia
Photometric identification of blue horizontal branch stars
Authors:Smith, K. W.; Bailer-Jones, C. A. L.; Klement, R. J.; Xue, X. X.
Language:English
Date of Publication (YYYY-MM-DD):2010
Title of Journal:Astronomy and Astrophysics
Journal Abbrev.:A&A
Volume:522
Start Page:id.A88
Review Status:Peer-review
Audience:Experts Only
Abstract / Description:We 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. Full Tables 7, A.3 and A.4 are only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via <A href="http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/522/A88">http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/522/A88</A>
Free Keywords:methods: statistical; stars: horizontal-branch; Galaxy: structure
External Publication Status:published
Document Type:Article
Communicated by:N. N.
Affiliations:MPI für Astronomie
Identifiers:URL:http://adsabs.harvard.edu/abs/2010A%26A...522A..88... [ID No:1]
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