Spectroscopic follow-up of statistically selected extremely metal-poor star candidates from GALAH DR3

TL;DR

This study validates a machine learning-based statistical method for efficiently identifying extremely metal-poor stars in large stellar surveys, confirmed through spectroscopic follow-up, with promising implications for future surveys.

Contribution

The paper demonstrates the effectiveness of a t-SNE based statistical selection method in identifying EMP stars from GALAH survey data, with successful spectroscopic validation.

Findings

Approximately one-third of candidates have [Fe/H] ≤ -2.75

Low contamination rate with less than 10% having [Fe/H] > -2

Five stars confirmed with [Fe/H] ≤ -3.0, including a main sequence turnoff star

Abstract

The advent of large-scale stellar spectroscopic surveys naturally leads to the implementation of machine learning techniques to isolate, for example, small sub-samples of potentially interesting stars from the full data set. A recent example is the application of the t-SNE statistical method to $\sim$600,000 stellar spectra from the GALAH survey in order to identify a sample of candidate extremely metal-poor (EMP, [Fe/H] $\leq$ -3) stars. We report the outcome of low-resolution spectroscopic follow-up of 83 GALAH EMP candidates that lack any previous metallicity estimates. Overall, the statistical selection is found to be efficient ($\sim$one-third of the candidates have [Fe/H] $\leq$ -2.75) with low contamination ($<$10% have [Fe/H] $>$ -2), and with a metallicity distribution function that is consistent with previous work. Five stars are found to have [Fe/H] $\leq$ -3.0, one of which is a main sequence turnoff star. Two other stars are revealed as likely carbon-enhanced metal-poor (CEMP) stars of type CEMP-$s$, and a known carbon star is re-identified. The results indicate that the statistical selection approach employed was successful, and therefore it can be applied to forthcoming even larger stellar spectroscopic surveys with the expectation of similar positive outcomes.