Photometric segregation of dwarf and giant FGK stars using the SVO Filter Profile Service and photometric tools

@ARTICLE{2024A&A...689A..93R,
      author = {{Rodrigo}, Carlos and {Cruz}, Patricia and {Aguilar}, John F. and {Aller}, Alba and {Solano}, Enrique and {G{\'a}lvez-Ortiz}, Maria Cruz and {Jim{\'e}nez-Esteban}, Francisco and {Mas-Buitrago}, Pedro and {Bayo}, Amelia and {Cort{\'e}s-Contreras}, Miriam and {Murillo-Ojeda}, Raquel and {Bonoli}, Silvia and {Cenarro}, Javier and {Dupke}, Renato and {L{\'o}pez-Sanjuan}, Carlos and {Mar{\'\i}n-Franch}, Antonio and {de Oliveira}, Claudia Mendes and {Moles}, Mariano and {Taylor}, Keith and {Varela}, Jes{\'u}s and {Rami{\'o}}, H{\'e}ctor V{\'a}zquez},
        title = "{Photometric segregation of dwarf and giant FGK stars using the SVO Filter Profile Service and photometric tools}",
      journal = {\aap},
    keywords = {methods: data analysis, techniques: photometric, astronomical databases: miscellaneous, virtual observatory tools, stars: fundamental parameters, stars: late-type, Solar and Stellar Astrophysics, Astrophysics of Galaxies},
        year = 2024,
        month = sep,
      volume = {689},
          eid = {A93},
        pages = {A93},
          doi = {10.1051/0004-6361/202449998},
archivePrefix = {arXiv},
      eprint = {2406.03310},
primaryClass = {astro-ph.SR},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2024A&A...689A..93R},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

In this work, published in Astronomy & Astrophysics, we present the power of the Spanish Virtual Observatory Filter Profile Service and associated photometric tools, and demonstrate their power to segregate FGK dwarf and giant stars using narrow-band photometry. Using machine-learning techniques such as the Gaussian mixture model and the support vector machine algorithms, we defined several criteria based on J-PAS colours to discriminate between dwarf and giant stars, selecting the five most promising colour-colour diagrams for this task and defining a criteria to maximise the separation.

Two-dimensional UMAP projection of our sample of giants (filled squares with darker contours) and dwarfs (crosses), colour-coded by effective temperature. Although there is an overlap for temperatures above 5500 K approximately, both classes are clearly distinguishable for lower temperatures.

Abstract

Aims. This paper is focused on the segregation of FGK dwarf and giant stars through narrow-band photometric data using the Spanish Virtual Observatory (SVO) Filter Profile Service and associated photometric tools.

Methods. We selected spectra from the MILES, STELIB, and ELODIE stellar libraries, and used SVO photometric tools to derive the synthetic photometry in 15 J-PAS narrow filters, which were especially selected to cover spectral features sensitive to gravity changes. Using machine-learning techniques as the Gaussian mixture model and the support vector machine, we defined several criteria based on J-PAS colours to discriminate between dwarf and giant stars.

Results. We selected five colour-colour diagrams that presented the most promising separation between both samples. Our results show an overall accuracy in the studied sample of ~0.97 for FGK stars, although a dependence on the luminosity type and the stellar effective temperature was found. We also defined a colour-temperature relation for dwarf stars with effective temperatures between 4 000 and 7 000 K, which allows one to estimate the stellar effective temperature from four J-PAS filters (J0450, J0510, J0550, and J0620). Additionally, we extended the study to M-type giant and dwarf stars, achieving a similar accuracy to that for FGK stars.