Classifications from Gaia & WISE data

Such work is only possible if the observed objects can be reliably classified. Spectroscopic observations are infeasible with more distant targets, and so we wish to determine whether machine-learning methods can classify massive stars using broadband infrared photometry.

We find that a Support Vector Machine classifier is capable of coarsely classifying massive stars with labels corresponding to hot, cool, and emission-line stars with high accuracy, while rejecting contaminating low-mass giants. Remarkably, 76% of emission-line stars can be recovered without the need for narrowband or spectroscopic observations. We classify a sample of ~2500 objects with no existing labels and identify 14 candidate emission-line objects.