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

Spina, L. ; Magrini, L. ; Sacco, G. G. ; Casali, G. ; Vallenari, A. ; Tautvaišiene, G. ; Jiménez-Esteban, F. ; Gilmore, G. ; Randich, S. and Feltzing, S. LU orcid , et al. (2022) In Astronomy and Astrophysics 668.
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Astrochemistry, Galaxy: abundances, Galaxy: disk, Methods: statistical, Stars: abundances
in
Astronomy and Astrophysics
volume
668
article number
A16
publisher
EDP Sciences
external identifiers
  • scopus:85145256977
ISSN
0004-6361
DOI
10.1051/0004-6361/202243316
language
English
LU publication?
yes
id
b777f7d3-092c-4618-ae99-364026365489
date added to LUP
2023-01-16 16:30:49
date last changed
2024-04-03 18:43:23
@article{b777f7d3-092c-4618-ae99-364026365489,
  abstract     = {{<p>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.</p>}},
  author       = {{Spina, L. and Magrini, L. and Sacco, G. G. and Casali, G. and Vallenari, A. and Tautvaišiene, G. and Jiménez-Esteban, F. and Gilmore, G. and Randich, S. and Feltzing, S. and Jeffries, R. D. and Bensby, T. and Bragaglia, A. and Smiljanic, R. and Carraro, G. and Morbidelli, L. and Zaggia, S.}},
  issn         = {{0004-6361}},
  keywords     = {{Astrochemistry; Galaxy: abundances; Galaxy: disk; Methods: statistical; Stars: abundances}},
  language     = {{eng}},
  publisher    = {{EDP Sciences}},
  series       = {{Astronomy and Astrophysics}},
  title        = {{The Gaia -ESO Survey : Chemical tagging in the thin disk: Open clusters blindly recovered in the elemental abundance space}},
  url          = {{http://dx.doi.org/10.1051/0004-6361/202243316}},
  doi          = {{10.1051/0004-6361/202243316}},
  volume       = {{668}},
  year         = {{2022}},
}