Galaxies in the Zone of Avoidance: Misclassifications using machine learning tools

Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manual approaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional concerns. These regions are known for high levels of interstellar extinction, star crowding, and limited data sets and studies. In this study, we explore the identification and classification of galaxies in the Zone of Avoidance (ZoA). In particular, we compare our results in the near-infrared with X-ray data. We analize the appearance of the objects classified as galaxies using machine learning by Zhang et al. (2021) in the Galactic disk and make a comparison with the visually confirmed galaxies from the VVV NIRGC (Baravalle et al. (2021). Our analysis, which includes the visual inspection of all sources catalogued as galaxies throughout the Galactic disk using machine learning techniques reveals significant differences. Only 4 galaxies were found in both the near-Infrared and X-ray data sets. Several specific regions of interest within the ZoA exhibit a high probability of being galaxies in X-ray data but closely resemble extended Galactic objects. The results indicate the difficulty of using machine learning methods for galaxy classification in the ZoA mainly due to the scarce information on galaxies behind the Galactic plane in the training set. They also stress the importance of considering specific factors that are present to improve the reliability and accuracy of future studies in this challenging region.