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

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 T eff , log g, [M/H], and v e sin i. 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 T eff , 0.06 dex for log g, 0.08 dex for [M/H], and 3 km/s for v e sin i for AFGK stars.