Contributed Talk - Splinter EScience

Friday, 17 September 2021, 10:00   (virtual ESc)

A Convolutional Neural Network Approach for Stellar Atmospheric Parameters and Lithium Abundance Determination

Samir Nepal, Guillaume Guiglion
Universität Potsdam, Leibniz-Institut für Astrophysik Potsdam (AIP)

The chemical element Lithium is of a great interest as its evolution in the Milky Way is not yet well understood. To help tighten the constrain on stellar and galactic chemical evolution models, numerous and precise Lithium abundance determination are necessary for stars in a large range of evolutionary stages and galactic populations. In the age of industrial stellar abundances, spectroscopic surveys such as GALAH, RAVE, and LAMOST have used data-driven methods to rapidly and precisely determine stellar labels (atmospheric parameters + abundances). The ultimate goal of this work is to prepare the machine learning ground for Lithium measurement in the context of the future spectroscopic surveys 4MOST and WEAVE. To do so, we develop a Convolution Neural Network (CNN) approach, based on stellar labels and GIRAFFE HR15 spectra of the 6th internal-release of the Gaia-ESO survey, to determine atmospheric parameters and Lithium abundances for ~40,000 stars. The HR15 setup is very well adapted for this purpose, being very similar to the HR red arm of both WEAVE and 4MOST. We show that the unique Lithium feature at 6708 Å is successfully singled out by the CNN, among the thousands of spectral features. Efficient Lithium measurements are performed in field and open cluster stars, as well as for rare objects like Lithium-rich giants. Such performances are achieved by meticulously building a high quality and homogeneous training sample. Our findings give very good insights for the future of 4MOST and WEAVE surveys in terms of Lithium analysis and science output. Due to the capacity to learn very complex relations and its flexibility, this method (CNN) can be easily adapted, with very little re-engineering, to include additional labels for instance rotational velocities, and other chemical abundances. A CNN can also be easily adapted to other spectroscopic surveys with different wavelength ranges.