The Gaia-ESO Survey: Chemical tagging in the thin disk

Open clusters blindly recovered in the elemental abundance space^⋆

¹ INAF – Padova Observatory, Vicolo dell’Osservatorio 5, 35122 Padova, Italy
e-mail: spina.astro@gmail.com
² INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze, Italy
³ Dipartimento di Fisica e Astronomia, Università degli Studi di Bologna, Via Gobetti 93/2, 40129 Bologna, Italy
⁴ INAF – Osservatorio di Astrofisica e Scienza dello Spazio, Via P. Gobetti 93/3, 40129 Bologna, Italy
⁵ Institute of Theoretical Physics and Astronomy, Vilnius University, Sauletekio Av. 3, 10257 Vilnius, Lithuania
⁶ Departamento de Astrofísica, Centro de Astrobiología (CSIC-INTA), 28692 Villanueva de la Cañada, Madrid, Spain
⁷ Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
⁸ Lund Observatory, Department of Astronomy and Theoretical Physics, Box 43 221 00 Lund, Sweden
⁹ Astrophysics Group, Keele University, Keele, Staffordshire ST5 5BG, UK
¹⁰ Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, ul. Bartycka 18, 00-716 Warsaw, Poland
¹¹ Dipartimento di Fisica e Astronomia, Università di Padova, Vicolo dell’Osservatorio 3, 35122 Padova, Italy

Received: 11 February 2022
Accepted: 24 March 2022

Abstract

Context. 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.

Aims. 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 some kind of guidance on different strategies of clustering analysis in the elemental abundance space.

Methods. 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 (e.g., choice of the algorithm, data preprocessing techniques, metric, space of data clustering), 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.

Results. 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 (i.e., escapers, binaries).

Conclusions. Our results indicate that there are chances of recovering open clusters’ members via clustering analysis in the elemental abundance space, albeit in a data set that has a very high fraction of cluster members compared to an average field star sample. Presumably, the performances of chemical tagging will further increase with higher quality data and more sophisticated clustering algorithms, which will likely became available in the near future.