ODUSSEAS: a machine learning tool to derive effective temperature and metallicity for M dwarf stars^★

¹ Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal
e-mail: alexandros.antoniadis@astro.up.pt
² Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
³ Instituto Federal do Paraná, Campus Foz do Iguaçu, 85860000 Foz do Iguaçu-PR, Brazil
⁴ Casimiro Montenegro Filho Astronomy Center, Itaipu Technological Park, 85867-900 Foz do Iguaçu-PR, Brazil

Received: 26 November 2019
Accepted: 19 February 2020

Abstract

Aims. The derivation of spectroscopic parameters for M dwarf stars is very important in the fields of stellar and exoplanet characterization. The goal of this work is the creation of an automatic computational tool able to quickly and reliably derive the T_eff and [Fe/H] of M dwarfs using optical spectra obtained by different spectrographs with different resolutions.

Methods. ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes) is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package “scikit-learn” for predicting the stellar parameters.

Results. We show that our tool is able to derive parameters accurately and with high precision, having precision errors of ~30 K for T_eff and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions of between 48 000 and 115 000 and a signal-to-noise ratio above 20.