2022/10/31 by M. Gebran, F. Paletou, Gebran, Marwan +7 · 1 citation
Engineering · Physics and Astronomy · #Astronomical Observations and Instrumentation #Astronomy and Astrophysical Research #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Solar and Stellar Astrophysics (astro-ph.SR) #Stellar, planetary, and galactic studies
paper · pdf · doi:10.48550/arxiv.2210.17470
openalex publication_date 2022/10/31 · openalex created_date 2022/11/06 · openalex updated_date 2026/07/28
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.