The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc

Abstract

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 VVVX survey. Using the VVV-NIRGC, we analyse by statistical methods the most relevant features for galaxy identification. This catalogue was used to train a CNN with image data and an XGBoost model with both photometric and morphological data and then to generate a dataset 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 extragalatic 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-NIR Galaxy Catalogue: Northern part of the Galactic disc comprising 1,003 new galaxies, with probabilities greater than 0.6 for either model, with visual inspection and with only 2 previously identified galaxies. In the future, we intend to apply this methodology to other areas of the VVVX survey.

12 pages, 14 figures, accepted in MNRAS

References in corpus (19)

• Deep Learning in Neural Networks: An Overview
• Trigonometric Parallaxes Of High-Mass Star Forming Regions: Our View Of The Milky Way
• Gaia Early Data Release 3: Summary of the contents and survey properties
• The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Analysis and Data Release 2
• Identification of Young Stellar Object candidates in the $Gaia$ DR2 x AllWISE catalogue with machine learning methods
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• Unsupervised star, galaxy, qso classification: Application of HDBSCAN
• AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised Morphological Classification of Galaxies and Synthetic Image Generation
• The PAU Survey: Photometric redshifts using transfer learning from simulations
• Variable star classification across the Galactic bulge and disc with the VISTA Variables in the Vía Láctea survey
• Searching for Extragalactic Sources in the VISTA Variables in the Vía Láctea Survey
• Deep NIR photometry of HI galaxies in the Zone of Avoidance
• The VVV Near-IR Galaxy Catalogue beyond the Galactic disk
• Confirmation of a cluster of galaxies hidden behind the Galactic bulge using the VVV Survey
• The First Galaxy Cluster Discovered by the VISTA Variables in the Vía Láctea Survey
• Overdensity of VVV galaxies behind the Galactic bulge
• A deep near-infrared view of the Ophiuchus galaxy cluster
• Automated classification of eclipsing binary systems in the VVV Survey
• Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV

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