Classification of Astronomical Objects in the Spitzer Taurus Survey Region Using Support Vector Machine

Astronomy and Astrophysics – Astronomy

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Scientific paper

The Spitzer Taurus Survey, containing several hundred thousand sources, is sensitive to detecting new young stellar objects in the Taurus cloud. However, the survey also contains numerous extragalactic objects, which are not easily distinguished from faint YSOs using standard color selection criteria. We tested the ability of Support Vector Machine (SVM) to distinguish class I, II, and III young stellar objects and galaxies in the Spitzer Taurus Survey. SVM is a method that classifies data by creating a maximum-margin hyperplane between classes in a multidimensional space. Our objects were classified using the radial basis function (RBF) kernel, on the basis of flux data from the Spitzer Catalog. We chose sources with few or no instances of missing flux data to train and test SVM. The sources included a set of galaxies that were identified as having color indices very similar to those of young stellar objects. SVM was able to distinguish objects with about 77% accuracy using 8 wavelengths of data from the 2MASS J band through the MIPS 24 micron band. When just the five wavelengths from the IRAS 3.6 micron band through the MIPS 24 micron band were used in order to include more objects, the classification error increased to over 30%. SVM could potentially be used to find candidates for young stellar objects of various classes using their flux data.
The Spitzer Space Telescope is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA.

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