SIMBAD references

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 percent 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.