Determination of metallicities of red giant stars using machine learning techniques applied to the narrow and broadband photometry of the S-PLUS survey

¹ Departamento de Astronomía, Universidad de La Serena, Av. J. Cisternas 1200 N, 1720236 La Serena, Chile
² Cerro Tololo Inter-American Observatory/NSF’s NOIRLab, Casilla 603, La Serena, Chile
³ Instituto Multidisciplinario de Investigación y Postgrado, Universidad de La Serena, Av. R. Bitrán 1305, 1720256 La Serena, Chile
⁴ Departamento de Astronomia, Instituto de Astronomia, Geofísica e Ciências Atmosféricas da USP, Cidade Universitária, 05508-900 São Paulo, SP, Brazil
⁵ NSF’s NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA
⁶ Observatório Nacional, Ministério da Ciência, Tecnologia, Inovação e Comunicações, Brazil
⁷ GMTO Corporation 465 N. Halstead Street, Suite 250 Pasadena, CA 91107, USA
⁸ Rubin Observatory Project Office, 950 N. Cherry Ave., Tucson, AZ 85719, USA
⁹ Departamento de Física, Universidade Federal de Santa Catarina, Florianópolis, SC 88040-900, Brazil

^★ Corresponding author; francisca.molinaj@userena.cl

Received: 11 June 2024
Accepted: 7 August 2024

Abstract

Aims. The aim of this study is to obtain metallicities of red giant stars from the Southern Photometric Local Universe Survey (S-PLUS) and to classify giant and dwarf stars using artificial neural networks applied to the S-PLUS photometry.

Methods. We combined the five broadband and seven narrow-band filters of S-PLUS – especially centred on prominent stellar spectral features – to train machine learning algorithms. The training catalogue was made by cross-matching the S-PLUS and Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey catalogues. The classification neural network uses the colours (J0378 - u), (J0395 - g), (J0410 - g), (J0515 - g), (J0660 - r), (g - z) and (r - i) as input features, whereas the network for metallicities uses the colours (J0378 - u), (J0395 - g), (J0410 - g), (J0515 - g), (J0660 - r), (u - g) and (r - z) as input features.

Results. The resulting network is capable of identifying ~99% of the giants in the test set. The network for determining the photometric metallicities of giant stars estimates metallicities in the test set a with a standard deviation of σ_giants ~ 0.07 dex with respect to the spectroscopic values. Finally, we used the trained artificial neural networks to generate a publicly available catalogue of 523 426 stars classified as red giant stars from S-PLUS, which we used to explore metallicity gradients in the Milky Way.