2023MNRAS.524..678D

The automated identification of extragalactic objects in large surveys provides reliable and reproducible samples of galaxies in less time than procedures involving human interaction. However, regions near the Galactic disc are more challenging due to the dust extinction. We present the methodology for the automatic classification of galaxies and non-galaxies at low Galactic latitude regions using both images and photometric and morphological near-IR data from the VISTA Variables in the Vía Láctea eXtended (VVVX) survey. Using the VVV NIR Galaxy Catalogue (VVV NIRGC), we analyse by statistical methods the most relevant features for galaxy identification. This catalogue was used to train a convolutional neural network with image data and an XGBoost model with both photometric and morphological data and then to generate a data set of extragalactic candidates. This allows us to derive probability catalogues used to analyse the completeness and purity as a function of the configuration parameters and to explore the best combinations of the models. As a test case, we apply this methodology to the Northern disc region of the VVVX survey, obtaining 172 396 extragalactic candidates with probabilities of being galaxies. We analyse the performance of our methodology in the VVV disc, reaching an F1-score of 0.67, a 65 per cent purity, and a 69 per cent completeness. We present the VVV NIRGC: Northern part of the Galactic disc comprising 1003 new galaxies, with probabilities greater than 0.6 for either model, with visual inspection and with only two previously identified galaxies. In the future, we intend to apply this methodology to other areas of the VVVX survey.