Examinando por Autor "Daza-Perilla I.V."

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    Automated classification of eclipsing binary systems in the VVV Survey
    (Oxford University Press, 2023-03) Daza-Perilla I.V.; Gramajo L.V.; Lares M.; Palma T.; Lopes, C.E. Ferreira; Minniti D.; Clariá J.J.
    With the advent of large-scale photometric surveys of the sky, modern science witnesses the dawn of big data astronomy, where automatic handling and discovery are paramount. In this context, classification tasks are among the key capabilities a data reduction pipeline must possess in order to compile reliable data sets, to accomplish data processing with an efficiency level impossible to achieve by means of detailed processing and human intervention. The VISTA Variables of the Vía Láctea Survey, in the southern part of the Galactic disc, comprises multiepoch photometric data necessary for the potential discovery of variable objects, including eclipsing binary systems (EBs). In this study, we use a recently published catalogue of one hundred EBs, classified by fine-tuning theoretical models according to contact, detached, or semidetached classes belonging to the tile d040 of the VVV. We describe the method implemented to obtain a supervised machine-learning model, capable of classifying EBs using information extracted from the light curves of variable object candidates in the phase space from tile d078. We also discuss the efficiency of the models, the relative importance of the features and the future prospects to construct an extensive data base of EBs in the VVV survey. © 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
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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.