Deep learning applications for stellar parameter determination: II-application to the observed spectra of AFGK stars

Abstract

In this follow-up article, 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{T}_{{\rm{eff}}}, logg\log g, [M/H]\left[M\hspace{0.1em}\text{/}\hspace{0.1em}H], and vesini{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 those of FGK stars from the spectroscopic survey of stars in the solar neighbourhood. The network model’s average accuracy on the stellar parameters is found to be as low as 80 K for Teff{T}_{{\rm{eff}}}, 0.06 dex for logg\log g, 0.08 dex for [M/H]\left[M\hspace{0.1em}\text{/}\hspace{0.1em}H], and 3 km/s for vesini{v}_{e}\sin i for AFGK stars.

Keywords

• data analyzis methods
• statistical
• methods
• deep learning methods
• spectroscopic techniques
• fundamental parameter stars