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<p>The Gaia mission led to a complete revision of our knowledge of the open cluster ecology in the Galaxy thanks to the access to new or more accurate structural and dynamical parameters of the stellar clusters. With access to a large set of stellar data in the Galaxy, we aim to reevaluate the identification and extraction of stellar clusters utilizing Gaia data. These new characterizations will be the baseline to build a new catalogue of local open clusters. The described method groups stars in an 8-fold space based on positions, velocities, magnitude, and colors using a DBSCAN algorithm. It optimizes the DBSCAN parameters and the data weighting to find the best solutions. It makes use of an Approximate Bayesian Computation (ABC) method because the traditional likelihood function is missing. The core and the external tidal tail memberships can be retrieved separately. The new unsupervised extraction method on Gaia data has proved to be efficient on benchmark stellar cluster targets. The method was implemented in the high performance Julia language and was released on the GitHub platform.</p>

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Extracting stellar clusters with gaia data: core and tail members

  • 期刊论文

  • 发表日期：

    2025年10月27日

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  • Stéphane Leon,
  • Taufiq Hidayat,
  • Amatul Firdausya Nur Cahyaningtyas,
  • Gilles Bergond,
  • Emilio J. Alfaro

摘要

The Gaia mission led to a complete revision of our knowledge of the open cluster ecology in the Galaxy thanks to the access to new or more accurate structural and dynamical parameters of the stellar clusters. With access to a large set of stellar data in the Galaxy, we aim to reevaluate the identification and extraction of stellar clusters utilizing Gaia data. These new characterizations will be the baseline to build a new catalogue of local open clusters. The described method groups stars in an 8-fold space based on positions, velocities, magnitude, and colors using a DBSCAN algorithm. It optimizes the DBSCAN parameters and the data weighting to find the best solutions. It makes use of an Approximate Bayesian Computation (ABC) method because the traditional likelihood function is missing. The core and the external tidal tail memberships can be retrieved separately. The new unsupervised extraction method on Gaia data has proved to be efficient on benchmark stellar cluster targets. The method was implemented in the high performance Julia language and was released on the GitHub platform.