Do open clusters have distinguishable chemical signatures?

TL;DR

This study investigates whether chemical signatures can distinguish stars from different open clusters, using machine learning on chemical data from 32 clusters to assess the feasibility of chemical tagging.

Contribution

It provides an empirical evaluation of the ability to identify stars' original clusters solely based on their chemical signatures using machine learning techniques.

Findings

Stars in open clusters are chemically homogeneous.

Machine learning can partially recover original clusters from chemical signatures.

Chemical tagging shows potential but has limitations in distinguishing all clusters.

Abstract

Past studies have already shown that stars in open clusters are chemically homogeneous (e.g. De Silva et al. 2006, 2007 and 2009). These results support the idea that stars born from the same giant molecular cloud should have the same chemical composition. In this context, the chemical tagging technique was proposed by Freeman & Bland-Hawthorn 2002. The principle is to recover disrupted stellar clusters by looking only to the stellar chemical composition. In order to evaluate the feasibility of this approach, it is necessary to test if we can distinguish between stars born from different molecular clouds. For this purpose, we studied the chemical composition of stars in 32 old and intermediate-age open clusters, and we applied machine learning algorithms to recover the original cluster by only considering the chemical signatures.