Star-galaxy separation strategies for WISE-2MASS all-sky infrared galaxy catalogues

We combine photometric information of the Wide-Field Infrared Survey Explorer (WISE) and Two Micron All Sky Survey (2MASS) all-sky infrared data bases, and demonstrate how to produce clean and complete galaxy catalogues for future analyses. Adding 2MASS colours to WISE photometry improves star-galaxy separation efficiency substantially at the expense of losing a small fraction of the galaxies. We find that 93 per cent of the WISE objects within W1 < 15.2 mag have a 2MASS match, and that a class of supervised machine learning algorithms, support vector machines (SVM), are efficient classifiers of objects in our multicolour data set. We constructed a training set from the Sloan Digital Sky Survey PhotoObj table with known star-galaxy separation, and determined redshift distribution of our sample from the Galaxy and Mass Assembly spectroscopic survey. Varying the combination of photometric parameters input into our algorithm we show that W1(WISE) - J(2MASS) is a simple and effective star-galaxy separator, capable of producing results comparable to the multidimensional SVM classification. We present a detailed description of our star-galaxy separation methods, and characterize the robustness of our tools in terms of contamination, completeness, and accuracy. We explore systematics of the full sky WISE-2MASS galaxy map, such as contamination from moon glow. We show that the homogeneity of the full sky galaxy map is improved by an additional J(2MASS) < 16.5 mag flux limit. The all-sky galaxy catalogue we present in this paper covers 21 200 deg(2) with dusty regions masked out, and has an estimated stellar contamination of 1.2 per cent and completeness of 70.1 per cent among 2.4 million galaxies with z(med) approximate to 0.14. WISE-2MASS galaxy maps with well controlled stellar contamination will be useful for spatial statistical analyses, including cross-correlations with other cosmological random fields, such as the cosmic microwave background. The same techniques also yield a statistically controlled sample of stars as well.