PREPRINT

Deep Learning application for stellar parameters determination: II- Application to observed spectra of AFGK stars

Marwan Gebran, Frédéric Paletou, Ian Bentley, Rose Brienza, Kathleen Connick

Submitted on 31 October 2022

Abstract

In this follow-up paper, we investigate the use of Convolutional Neural Network for deriving stellar parameters from observed spectra. Using hyperparameters determined previously, we have constructed a Neural Network architecture suitable for the derivation of Teff, log g, [M/H], and vesini. The network was constrained by applying it to databases of AFGK synthetic spectra at different resolutions. Then, parameters of A stars from Polarbase, SOPHIE, and ELODIE databases are derived as well as FGK stars from the Spectroscopic Survey of Stars in the Solar Neighbourhood. The network model average accuracy on the stellar parameters are found to be as low as 80 K for Teff , 0.06 dex for log g, 0.08 dex for [M/H], and 3 km/s for vesini for AFGK stars.

Preprint

Comment: 13 pages, 7 figures. Accepted for publication in Open Astronomy, De Gruyter

Subjects: Astrophysics - Solar and Stellar Astrophysics; Astrophysics - Instrumentation and Methods for Astrophysics; Physics - Computational Physics