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A support vector machine to search for metal-poor galaxies.
- Source :
-
Monthly Notices of the Royal Astronomical Society: Letters . Aug2014, Vol. 444 Issue 1, pL49-L53. 1p. - Publication Year :
- 2014
-
Abstract
- To develop a fast and reliable method for selecting metal-poor galaxies (MPGs), especially in large surveys and huge data bases, a support vector machine (svm) supervized learning algorithms is applied to a sample of star-forming galaxies from the Sloan Digital Sky Survey data release 9 provided by the Max Planck Institute and the Johns Hopkins University (http://www.sdss3.org/dr9/spectro/spectroaccess.php). A two-step approach is adopted: (i) the svm must be trained with a subset of objects that are known to be either MPGs or metal-rich galaxies (MRGs), treating the strong emission line flux measurements as input feature vectors in n-dimensional space, where n is the number of strong emission line flux ratios. (ii) After training on a sample of star-forming galaxies, the remaining galaxies are classified in the automatic test analysis as either MPGs or MRGs using a 10-fold cross-validation technique. For target selection, we have achieved an acquisition accuracy for MPGs of ∼96 and ∼95 per cent for an MPG threshold of 12 + log(O/H) = 8.00 and 12 + log(O/H) = 8.39, respectively. Running the code takes minutes in most cases under the matlab 2013a software environment. The code in the Letter is available on the web (http://fshi5388.blog.163.com). The svm method can easily be extended to any MPGs target selection task and can be regarded as an efficient classification method particularly suitable for modern large surveys. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 17453925
- Volume :
- 444
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Monthly Notices of the Royal Astronomical Society: Letters
- Publication Type :
- Academic Journal
- Accession number :
- 97894817
- Full Text :
- https://doi.org/10.1093/mnrasl/slu096