Access to Astronomical Catalogues

← Click to display the menu
J/A+A/522/A88       Photometric identification of BHB stars      (Smith+, 2010)

Photometric identification of blue horizontal branch stars. Smith K.W., Bailer-Jones C.A.L., Klement R.J., Xue X.X. <Astron. Astrophys. 522, A88 (2010)> =2010A&A...522A..88S
ADC_Keywords: Milky Way ; Stars, horizontal branch ; Photometry, SDSS Keywords: methods: statistical - stars: horizontal-branch - Galaxy: structure Abstract: 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. Description: A catalogue of classifications of candidate BHB stars is presented. The sample is drawn from SDSS data release 7, and has then been filtered for consistency with the classifier training set as described in the paper. File Summary:
FileName Lrecl Records Explanations
ReadMe 80 . This file table7.dat 314 294652 Catalogue of SDSS BHB candidates tablea3.dat 78 152 Model data for the one-class SVM (support vector machine) model tablea4.dat 78 2645 Model data for two-class SVM model
See also: II/294 : The SDSS Photometric Catalog, Release 7 (Adelman-McCarthy+, 2009) : SDSS Home Page Byte-by-byte Description of file: table7.dat
Bytes Format Units Label Explanations
1- 18 A18 --- ObjID SDSS ObjID from PhotoObj table 23- 26 I4 --- plate ?=0 SDSS plate from specObj table when available 31- 35 I5 --- MJD ?=0 SDSS MJD from specObj table when available 40- 42 I3 --- fiber ?=0 SDSS fiber from specObj table when available 47- 58 F12.8 deg RAdeg Right ascension in decimal degrees (J2000.0) (1) 63- 74 F12.8 deg DEdeg Declination in decimal degrees (J2000.0) (1) 79- 90 F12.8 deg GLON Galactic longitude (1) 95-106 F12.8 deg GLAT Galactic latitude (1) 111-117 F7.4 mag umag SDSS u-band psf magnitude 120-126 F7.4 mag gmag SDSS g-band psf magnitude 129-135 F7.4 mag rmag SDSS r-band psf magnitude 138-144 F7.4 mag imag SDSS i-band psf magnitude 147-153 F7.4 mag zmag SDSS z-band psf magnitude 156-162 F7.4 mag e_umag SDSS u-band magnitude error 165-171 F7.4 mag e_gmag SDSS g-band magnitude error 174-180 F7.4 mag e_rmag SDSS r-band magnitude error 183-189 F7.4 mag e_imag SDSS i-band magnitude error 192-198 F7.4 mag e_zmag SDSS z-band magnitude error 201-207 F7.4 mag uext u-band extinction from SDSS pipeline (2) 210-216 F7.4 mag gext g-band extinction from SDSS pipeline (2) 219-225 F7.4 mag rext r-band extinction from SDSS pipeline (2) 228-234 F7.4 mag iext i-band extinction from SDSS pipeline (2) 237-243 F7.4 mag zext z-band extinction from SDSS pipeline (2) 246-250 A5 --- Type Object category, from Xue et al., 2008ApJ...684.1143X (3) 255-260 F6.4 --- Psvm Probability of BHB from SVM (support vector machine) model (4) 265-270 F6.4 --- e_Psvm Standard deviation of Psvm for 10 trials (4) 275-280 F6.4 --- Prior 2d prior applied to probability. 285-291 F7.5 --- modP Psvm probability modified by prior (5) 296-301 F6.2 kpc Dist Estimated distance in kpc (6) 306-314 F9.4 --- e_Dist Fractional (relative) error on the distance
Note (1): RA, DE, l, and b are taken from SDSS PhotoObj table Note (2): Extinctions calculated by comparing model magnitude (from PhotoObj table) to dereddened magnitude dered_u etc. Note (3): Category from treatment by Xue et al. (2008ApJ...684.1143X): None = does not appear, BHB = classified as BHB by both D0.2-fm and cγ-bγ methods, BS = classified as blue straggler from D0.2-fm method, MS = classified as main sequence star from D0.2-fm method, Other = classified as BHB star by D0.2-fm method but not from cγ-bγ method. Note (4): If less than 10-4 set to 10-4 Note (5): If less than 10-5 set to 10-5 Note (6): Under the assumption the star is a BHB. Not valid for non-BHBs cases.
Byte-by-byte Description of file: tablea3.dat tablea4.dat
Bytes Format Units Label Explanations
1- 14 F14.8 --- y.alpha Product of weight and class label for each support vector 19- 30 F12.8 --- u-g Standardized dereddened u-g colour 35- 46 F12.8 --- g-r Standardized dereddened g-r colour 51- 62 F12.8 --- r-i Standardized dereddened r-i colour 67- 78 F12.8 --- i-z Standardized dereddened i-z colour
Acknowledgements: K.W. Smith, smith(at) References: Xue, Rix, Zhao, et al., 2008ApJ...684.1143X
(End) Patricia Vannier [CDS] 05-Aug-2010
The document above follows the rules of the Standard Description for Astronomical Catalogues.From this documentation it is possible to generate f77 program to load files into arrays or line by line

catalogue service