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

TL;DR

This paper demonstrates the use of convolutional neural networks to accurately determine stellar parameters from observed spectra of AFGK stars, improving the efficiency of stellar characterization.

Contribution

It introduces a CNN architecture optimized for stellar parameter estimation from observed spectra, validated on multiple star databases, with high accuracy results.

Findings

Achieved 80 K accuracy for Teff

Achieved 0.06 dex accuracy for log g

Achieved 3 km/s accuracy for vesini

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.