Machine learning to identify symbiotic stars

In this second paper in a series of papers based on the most-up-to-date catalogue of symbiotic stars (SySts), we present a new approach for identifying and distinguishing SySts from other H{alpha} emitters in photometric surveys using machine learning algorithms such as classification tree, linear discriminant analysis, and K-nearest neighbour. The motivation behind this work is to seek for possible colour indices in the regime of near- and mid-infrared covered by the 2MASS and WISE surveys.

A number of diagnostic colour-colour diagrams are generated for all the known Galactic SySts and several classes of stellar objects that mimic SySts such as planetary nebulae, post-AGB, Mira, single K and M giants, cataclysmic variables, Be, AeBe, YSO, weak and classical T Tauri stars, and Wolf-Rayet. The classification tree algorithm unveils that primarily J-H, W1-W4, and K_s_-W3, and secondarily, H-W2, W1-W2, and W3-W4 are ideal colour indices to identify SySts.