Sep 5 – 7, 2023
Institute of Cosmos Sciences (ICCUB-IEEC)
Europe/Madrid timezone

Analysis and Classification of Hot Subdwarfs Using Artificial Intelligence Techniques and Gaia DR3 data (poster pitch, online)

Speaker

Carlos Viscasillas Vázquez (Vilnius University)

Description

In this study, we present an analysis of hot subdwarf stars (hot sds) based on the catalogue provided by Solano et al. (2022). Our analysis includes the utilization of the newly available Gaia DR3 data including the treatment of BP/RP spectra using artificial intelligence (AI) techniques. The AI techniques employed in this study involve Self Organized Maps (SOM) and software tools accessible to our collaboration group. This collaboration involves researchers of the Galician Group for the Gaia satellite (GGG), from the University of A Coruña (UDC) and the University of Vigo (UVIGO), in association with the Gaia DPAC consortium, of the Spanish Virtual Observatory (SVO) and of the University of Vilnius (Lithuania).

Our current work focuses on the development of a novel classification method for hot subdwarfs in binary systems, employing supervised machine learning (ML) techniques. Subsequently, we calculate the distribution of binary probabilities. For training purposes, we selected samples from Solano et al. (2022) and Drilling et al. (2013), and we applied our methodes to a larger catalogue of over 39,000 blue candidates as presented in Geier et al. (2019). We plan to compare our results with the outcomes derived from SOM techniques and also in the context of Virtual Observatory (VO) techniques as explored in Solano et al. (2022). These analyses aim to contribute to a better understanding of hot subdwarfs evolution and to enhance our knowledge of binary systems within this class, using advanced AI and data mining methodologies.

This abstract is framed within a Short-Term Scientific Mission (STSM), as part of a research exchange between members of the GGG consortium, the SVO and a visitor (presenting author) from the University of Vilnius, aiming to strengthen scientific collaboration and to advance the field of hot subdwarf classification using artificial intelligence techniques.

References:
Álvarez, M. A. ; Dafonte, C.; Manteiga, M.; et al. (2022). NC&A 34.
Drilling, J. S. ; Jeffery, C. S.; Heber, U. et al. (2013). A&A 551, A31.
Geier, S. ; Raddi, R.; Gentile Fusillo, N. P. ; Marsh, T. R. (2019). A&A, 621, A38.
Solano, E. ; Ulla, A. ; Pérez-Fernández, E. et al. (2022). MNRAS 514, 3.

Acknowledgments:
CVV and AUM acknowledge the MW-Gaia COST Action CA18104.

Primary authors

Carlos Viscasillas Vázquez (Vilnius University) Enrique Solano (Centro de Astrobiología) Ana Ulla-Miguel (Universidade de Vigo) Marco Antonio Álvarez González (University of A Coruña) Raúl Santoveña Gómez (University of A Coruña) Minia Manteiga Outeiro (University of A Coruña) José Carlos Dafonte Vázquez (University of A Coruña) Esther Perez-Fernández (Universidade de Vigo) Carlos Rodrigo (Centro de Astrobiologı́a) Alba Aller (Centro de Astrobiologı́a) Raquel Oreiro (Universidade de Vigo)

Presentation materials