학술논문

Deep Learning application for stellar parameters determination: II- Application to observed spectra of AFGK stars
Document Type
Working Paper
Source
Subject
Astrophysics - Solar and Stellar Astrophysics
Astrophysics - Instrumentation and Methods for Astrophysics
Physics - Computational Physics
Language
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.
Comment: 13 pages, 7 figures. Accepted for publication in Open Astronomy, De Gruyter