The Gaia-ESO Survey: Chemical tagging in the thin disk. Open clusters blindly recovered in the elemental abundance space

TL;DR

This study demonstrates that chemical tagging can effectively recover open cluster members in the thin disk using high-quality elemental abundance data and optimized clustering strategies, advancing the potential of chemical tagging in galactic archaeology.

Contribution

It introduces the first blind clustering-based method to recover open cluster members in the thin disk using Gaia-ESO survey data and evaluates strategies to improve chemical tagging performance.

Findings

Open clusters can be recovered using clustering in elemental abundance space.

Manhattan metric and careful dimensional selection improve recovery success.

New cluster members were identified through the analysis.

Abstract

The chemical makeup of a star provides the fossil information of the environment where it formed. Under this premise, it should be possible to use chemical abundances to tag stars that formed within the same stellar association. This idea - known as chemical tagging - has not produced the expected results, especially within the thin disk where open stellar clusters have chemical patterns that are difficult to disentangle. The ultimate goal of this study is to probe the feasibility of chemical tagging within the thin disk population using high-quality data from a controlled sample of stars. We also aim at improving the existing techniques of chemical tagging and giving guidance on different strategies of clustering analysis in the elemental abundance space. Here we develop the first blind search of open clusters' members through clustering analysis in the elemental abundance space using the OPTICS algorithm applied to data from the Gaia-ESO survey. First, we evaluate different strategies of analysis, determining which ones are more performing. Second, we apply these methods to a data set including both field stars and open clusters attempting a blind recover of as many open clusters as possible. We show how specific strategies of data analysis can improve the final results. Specifically, we demonstrate that open clusters can be more efficaciously recovered with the Manhattan metric and on a space whose dimensions are carefully selected. Using these (and other) prescriptions we are able to recover open clusters hidden in our data set and find new members of these stellar associations. Our results indicate that there are chances of recovering open clusters' members via clustering analysis in the elemental abundance space. Presumably, the performances of chemical tagging will further increase with higher quality data and more sophisticated clustering algorithms.