Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet stars. Issue 2 (26th September 2017)

• Record Type: Journal Article
• Authors: Morello, Giuseppe; Morris, P. W.; Van Dyk, S. D.; Marston, A. P.; Mauerhan, J. C.
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 473:Issue 2(2018)
• Page Start: 2565
• Page End: 2574
• Publication Date: 2017-09-26
• Subjects: methods: observational -- methods: statistical -- stars: evolution -- stars: massive -- stars: Wolf–Rayet -- infrared: stars; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stx2474
• Languages: English
• ISSNs: 0035-8711

Abstract

Abstract: We have investigated and applied machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet (WR) candidates. Objects taken from the Spitzer Galactic Legacy Infrared Midplane Survey Extraordinaire (GLIMPSE) catalogue of the infrared objects in the Galactic plane can be classified into different stellar populations based on the colours inferred from their broad-band photometric magnitudes [ J, H and K s from 2 Micron All Sky Survey (2MASS), and the four Spitzer /IRAC bands]. The algorithms tested in this pilot study are variants of the k -nearest neighbours approach, which is ideal for exploratory studies of classification problems where interrelations between variables and classes are complicated. The aims of this study are (1) to provide an automated tool to select reliable WR candidates and potentially other classes of objects, (2) to measure the efficiency of infrared colour selection at performing these tasks and (3) to lay the groundwork for statistically inferring the total number of WR stars in our Galaxy. We report the performance results obtained over a set of known objects and selected candidates for which we have carried out follow-up spectroscopic observations, and confirm the discovery of four new WR stars.