Searching For s-Process-Enhanced Metal-Poor Stars

TL;DR

This paper develops methods to estimate barium and strontium abundances in metal-poor stars using medium-resolution spectra, comparing regression and neural network techniques.

Contribution

It introduces a calibration approach for abundance ratios using line indices and compares regression with neural networks, highlighting the latter's advantages.

Findings

Neural networks outperform regression in estimating element abundances.

A calibration between line indices and abundance ratios was successfully established.

Preliminary results demonstrate the feasibility of medium-resolution spectral analysis for chemical abundances.

Abstract

We present preliminary results for estimation of barium ([Ba/Fe]) and strontium ([Sr/Fe]) abundances ratios using medium-resolution spectra (1-2 {\AA}). We established a calibration between the abundance ratios and line indices for Ba and Sr, using multiple regression and artificial neural network techniques. A comparison between the two techniques (showing the advantage of the latter), as well as a discussion of future work, is presented.