The NEMESIS general YSO catalogue - I. Supervised classification with deep learning methods

Abstract

In modern large-scale surveys, young stellar objects (YSOs) are mostly identified by the infrared excess in their spectral energy distributions (SEDs). To this end, YSO identification and classification were mostly based on certain colours, resulting in wavelength-specific methods. Using all available data allows us to construct a uniformly classified YSO catalogue by applying the same identification method to heterogeneous samples, reducing systematic differences and enabling more consistent studies of YSO populations and star formation. Our goal is to develop a method that takes advantage of the heterogeneous data available in the VizieR database and to provide a tool that can be used to identify YSOs with high accuracy. We also aim to create the largest homogeneous catalogue of YSOs. We use the CDS VizieR and NASA/IPAC IRSA to collect data for a large number of objects of various types. The data collected included SEDs, light curves, and direct imaging and were used to build different image representations. These were used to teach deep learning algorithms to differentiate between YSOs and all other types of sources. Architectures from the PyTorch library and custom-built convolutional neural networks were applied to create an ensemble of binary classifiers specialised at identifying YSOs. The resulting method is able to automatically identify YSO candidates with an accuracy higher than 90\% while contamination is identified with an accuracy higher than 99\%. We applied our classifier to numerous YSO catalogues to confirm the youth of the stars and to create the largest catalogue of homogeneously identified potential YSOs ever -- the NEMESIS General YSO catalogue contains 274\,408 unique, high reliability YSOs. Their positions on the colour-magnitude diagrams, and their distribution relative to the local Galactic structures, were also used to test their young nature.