Examinando por Autor "Baravalle, L."

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    A deep near-infrared view of the Ophiuchus galaxy cluster
    (EDP Sciences, 2022-07) Galdeano, D.; Coldwell, G.; Duplancic, F.; Alonso, S.; Pereyra, L.; Minniti, D.; Zelada Bacigalupo, R.; Valotto, C.; Baravalle, L.; Alonso, M.V.; Nilo Castellón, J.L.
    Context. The Ophiuchus cluster of galaxies, located at low latitudes in the direction of the Galactic bulge, has been relatively poorly studied in comparison with other rich galaxy clusters, such as Coma, Virgo, and Fornax, despite being the second brightest X-ray cluster in the sky. Aims. Our aim is perform a study of the hidden galaxy population of the massive cluster Ophiuchus located in the Zone of Avoidance. Methods. Deep near-infrared images and photometry from the VISTA Variables in the Vía Láctea eXtended (VVVX) survey were used to detect galaxy member candidates of the Ophiuchus cluster up to 2 Mpc from the cD galaxy 2MASX J17122774-2322108 using criteria from a past paper to select the galaxies among the foreground sources. We also perform a morphological visual classification and generate color-magnitude diagrams and density profiles. Results. We identify 537 candidate galaxy members of the Ophiuchus cluster up to 2 Mpc from the cD galaxy, increasing by a factor of seven the number of reported Ophiuchus galaxies. In addition, we performed a morphological classification of these galaxy candidates finding that the fraction of ellipticals reaches more than 60% in the central region of the cluster. On the other hand, the fraction of spirals fraction is lower than 20%, remaining almost constant throughout the cluster. Moreover, we study the red sequence of galaxy member candidates and use mock catalogs to explore the density profile of the cluster, finding that the value derived from the mock catalog toward an overdense region is in agreement with the galaxy excess of the central zone of the Ophiuchus cluster. Conclusions. Our investigation of the hidden population of Ophiuchus galaxies underscores the importance of this cluster as a prime target for future photometric and spectroscopic studies. Moreover the results of this work highlight the potential of the VVVX survey to study extragalactic objects in the Zone of Avoidance. © 2022 D. Galdeano et al.
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    Galaxies in the zone of avoidance: Misclassifications using machine learning tools
    (EDP Sciences, 2024-06) Marchant Cortés, P.; Nilo Castellón, J.L.; Alonso, M.V.; Baravalle, L.; Villalon, C.; Sgró, M.A.; Daza-Perilla, I.V.; Soto, M.; Milla Castro, F.; Minniti, D.; Masetti, N.; Valotto, C.; Lares, M.
    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.