The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Thomas Bensby. Profile photo.

Thomas Bensby

Senior lecturer

Thomas Bensby. Profile photo.

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

Author

  • L. Spina
  • L. Magrini
  • G. G. Sacco
  • G. Casali
  • A. Vallenari
  • G. Tautvaišiene
  • F. Jiménez-Esteban
  • G. Gilmore
  • S. Randich
  • S. Feltzing
  • R. D. Jeffries
  • T. Bensby
  • A. Bragaglia
  • R. Smiljanic
  • G. Carraro
  • L. Morbidelli
  • S. Zaggia
Show all

Summary, in English

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.

Department/s

  • Lund Observatory - Has been reorganised

Publishing year

2022-12

Language

English

Publication/Series

Astronomy and Astrophysics

Volume

668

Document type

Journal article

Publisher

EDP Sciences

Topic

  • Astronomy, Astrophysics and Cosmology

Keywords

  • Astrochemistry
  • Galaxy: abundances
  • Galaxy: disk
  • Methods: statistical
  • Stars: abundances

Status

Published

ISBN/ISSN/Other

  • ISSN: 0004-6361