Deep learning analyses of synthetic spectral libraries with an application to the Gaia-ESO database

Abstract

In the era of stellar spectroscopic surveys, synthetic spectral libraries will form the basis for the derivation of the stellar parameters and chemical abundances. In this thesis, four popular synthetic grids (INTRIGOSS, FERRE, AMBRE, and PHOENIX) are used in a deep learning prediction framework ("StarNet"), and compared in an application to observational optical spectra from the Gaia-ESO survey. The stellar parameters for temperature, surface gravity, metallicity, radial velocity, rotational velocity, and [$\alpha$/Fe] are determined simultaneously for FGK type dwarfs and giants. StarNet was modified from its application to SDSS APOGEE infrared spectra, not only to optical wavelengths, but also to mitigate the differences in the sampling between the synthetic grids and the observed spectra, and by augmenting the grids with realistic observational signatures, in an attempt to incorporate both modelling and statistical uncertainties as part of the training. When applied to spectra from the Gaia-ESO spectroscopic survey and the Gaia-ESO benchmark stars, the INTRIGOSS-trained StarNet showed the best results with the least scatter. Training with the FERRE synthetic grid produces similarly accurate predictions (followed closely by the AMBRE grid), but over a wider range in stellar parameters and spectroscopic wavelengths. This is an exciting and encouraging result for the direct application of synthetic spectra to the analysis of the planned spectroscopic surveys in the coming decade (WEAVE, 4MOST, PFS, and MSE). In the future, improvements in the underlying physics that generates these synthetic grids can be incorporated for consistent high precision stellar parameters and chemical abundances from machine learning and other sophisticated data analysis tools.