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

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 sp...

Full description

Saved in:
Bibliographic Details
Published in:Astronomy and astrophysics (Berlin) 2020-04, Vol.636, p.A9
Main Authors: Antoniadis-Karnavas, A., Sousa, S. G., Delgado-Mena, E., Santos, N. C., Teixeira, G. D. C., Neves, V.
Format: Article
Language:English
Subjects:
Absorption
Dwarf stars
Extrasolar planets
Machine learning
Metallicity
Parameters
Red dwarf stars
Signal to noise ratio
Software
Spectrographs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0004-6361
1432-0746
DOI:10.1051/0004-6361/201937194