Detection of hot subdwarf binaries and sdB stars using machine learning methods and a large sample of Gaia XP spectra
M. Ambrosch, C. Viscasillas Vázquez, E. Solano, A. Ulla, X. Pérez-Couto, E. Pérez-Fernández, A. Medžiūnas, M. Manteiga, C. Dafonte, A. Drazdauskas, L. Magrini, Š. Mikolaitis, and V. Šatas
TBA (2026)
https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202558282
Our goal is to identify patterns in Gaia XP spectra, investigate binarity, and assess the influence of parameters such as temperature, helium abundance, and variability. We analyse approximately 20 000 hot subdwarf candidates selected from the literature, combining Gaia XP data with published parameters.
Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data
C. Viscasillas Vázquez, E. Solano, A. Ulla, M. Ambrosch, M. A. Álvarez, M. Manteiga, L. Magrini, R. Santoveña-Gómez, C. Dafonte, E. Pérez-Fernández, A. Aller, A. Drazdauskas, Š. Mikolaitis, and C. Rodrigo
A&A, 691, A223 (2024)
https://www.aanda.org/articles/aa/full_html/2024/11/aa51247-24/aa51247-24.html
This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples.