Magnificent beasts of the Milky Way: Hunting down stars with unusual infrared properties using supervised machine learning

Sammanfattning: The significant increase of astronomical data necessitates new strategies and developments to analyse a large amount of information, which no longer is efficient if done by hand. Supervised machine learning is an example of one such modern strategy. In this work, we apply the classification technique on Gaia+2MASS+WISE data to explore the usage of supervised machine learning on large astronomical archives. The idea is to create an algorithm that recognises entries with unusual infrared properties which could be interesting for follow-up observations. The programming is executed in MATLAB and the training of the algorithms in the classification learner application of MATLAB. Each catalogue; Gaia+2MASS+WISE contains ~109, 5×108 and 7×108 (The European Space Agency 2019, Skrutskie et al. 2006, R. M. Cutri IPAC/Caltech) entries respectively. The algorithms searches through a sample from these archives consisting of 765266 entries, corresponding to objects within a <500 pc range. The project resulted in a list of 57 entries with unusual infrared properties, out of which 8 targets showed none of the four common features that provide a natural physical explanation to the unconventional energy distribution. After more comprehensive studies of the aforementioned targets, we deem it necessary for further studies and observations on 2 out of the 8 targets (Nr.1 and Nr.8 in table 3) to establish their true nature. The results demonstrate the applicability of machine learning in astronomy as well as suggesting a sample of intriguing targets for further studies.