A neural network approach to determining photometric metallicities of M-type dwarf stars
[Context] M dwarfs are the most abundant stars in the Galaxy and serve as key targets for stellar and exoplanetary studies. It is particularly challenging to determine their metallicities because their spectra are complex. For this reason, several authors have focused on photometric estimates of the...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/398638 |
| Acceso en línea: | http://hdl.handle.net/10261/398638 https://api.elsevier.com/content/abstract/scopus_id/105007510451 |
| Access Level: | acceso abierto |
| Palabra clave: | Hertzsprung-Russell and C-M diagrams Stars: abundances Stars: fundamental parameters Stars: late-type Stars: low-mass |
| Sumario: | [Context] M dwarfs are the most abundant stars in the Galaxy and serve as key targets for stellar and exoplanetary studies. It is particularly challenging to determine their metallicities because their spectra are complex. For this reason, several authors have focused on photometric estimates of the M-dwarf metallicity. Although artificial neural networks have been used in the framework of modern astrophysics, their application to a photometric metallicity estimate for M dwarfs remains unexplored. |
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