Galaxies in the zone of avoidance: Misclassifications using machine learning tools

P. MARCHANT CORTÉS; CASTELLÓN, J L NILO; ALONSO, M V; BARAVALLE, L D; VILLALON, C; SGRÓ, M A; DAZA PERILLA I.V; SOTO, M; F. MILLA CASTRO; MINNITI, D; N. MASETTI; VALOTTO, C; LARES, M

ASTRONOMY AND ASTROPHYSICS

EDP SCIENCES S A

Lugar: Paris; Año: 2024

0004-6361

Context. Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manualapproaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional concerns. Theseregions are known for high levels of interstellar extinction, star crowding, and limited data sets and studies.Aims. In this study, we explore the identification and classification of galaxies in the zone of avoidance (ZoA). In particular, wecompare our results in the near-infrared (NIR) with X-ray data.Methods. We analyzed the appearance of objects in the Galactic disk classified as galaxies using a published machine-learning (ML)algorithm and make a comparison with the visually confirmed galaxies from the VVV NIRGC catalog.Results. Our analysis, which includes the visual inspection of all sources cataloged as galaxies throughout the Galactic disk using MLtechniques reveals significant differences. Only four galaxies were found in both the NIR and X-ray data sets. Several specific regionsof interest within the ZoA exhibit a high probability of being galaxies in X-ray data but closely resemble extended Galactic objects.Our results indicate the difficulty in using ML methods for galaxy classification in the ZoA, which is mainly due to the scarcity ofinformation on galaxies behind the Galactic plane in the training set. They also highlight the importance of considering specific factorsthat are present to improve the reliability and accuracy of future studies in this challenging region.