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

¹ Departamento Astronomía, Facultad de Ciencias, Universidad de La Serena, Av. Juan Cisternas 1200, La Serena, Chile
e-mail: p.marchantcortes.9@gmail.com
² Instituto de Astronomía Teórica y Experimental (IATE-CONICET), Laprida 854, X5000BGR Córdoba, Argentina
³ Observatorio Astronómico de Córdoba, Universidad Nacional de Córdoba, Laprida 854, X5000BGR Córdoba, Argentina
⁴ Instituto de Investigación en Astronomía y Ciencias Planetarias, Universidad de Atacama, Copayapu 485, Copiapó, Chile
⁵ Instituto de Astrofísica, Facultad de Ciencias Exactas, Universidad Andrés Bello, Av. Fernandez Concha 700, Las Condes, Santiago, Chile
⁶ Vatican Observatory, 00120 Vatican City State, Italy
⁷ Departamento de Física, Universidade Federal de Santa Catarina, Trinidade, 88040-900 Florianopolis, Brazil
⁸ INAF – Osservatorio di Astrofisica e Scienza dello Spazio, Via Piero Gobetti 101, 40129 Bologna, Italy

Received: 16 November 2023
Accepted: 28 February 2024

Abstract

Context. 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.

Aims. 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 (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 ML techniques reveals significant differences. Only four galaxies were found in both the NIR 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. Our results indicate the difficulty in using ML methods for galaxy classification in the ZoA, which is mainly due to the scarcity of information on galaxies behind the Galactic plane in the training set. They also highlight the importance of considering specific factors that are present to improve the reliability and accuracy of future studies in this challenging region.