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[Paper Review] Open clusters with Hipparcos I. Mean astrometric parameters

N. Robichon, U. Bastian|arXiv (Cornell University)|Mar 9, 1999
Stellar, planetary, and galactic studies2 references3 citations
TL;DR

This paper presents high-precision mean astrometric parameters—parallaxes and proper motions—for 18 open clusters within 500 pc, using Hipparcos satellite data. It reports parallax precisions of 0.2–0.5 mas and proper motion precisions of 0.1–0.5 mas/yr, with no significant systematic biases found, offering critical calibration data for photometric parallaxes and kinematic studies of nearby stellar systems.

ABSTRACT

New memberships, mean parallaxes and proper motions of all 9 open clusters closer than 300 pc (except the Hyades) and 9 rich clusters between 300 and 500 pc have been computed using Hipparcos data. Precisions, ranging from 0.2 to 0.5 mas for parallaxes and 0.1 to 0.5 mas/yr for proper motions, are of great interest for calibrating photometric parallaxes as well as for kinematical studies. Careful investigations of possible biases have been performed and no evidence of significant systematic errors on the mean cluster parallaxes has been found. The distances and proper motions of 32 more distant clusters, which may be used statistically, are also indicated.

Motivation & Objective

  • To determine accurate mean parallaxes and proper motions for nearby open clusters using Hipparcos astrometric data.
  • To assess the precision and reliability of Hipparcos measurements for cluster mean parameters.
  • To calibrate photometric parallaxes by providing high-accuracy distance measurements for nearby clusters.
  • To investigate potential systematic errors in mean cluster parallaxes derived from Hipparcos data.
  • To extend statistical astrometric solutions to 32 more distant clusters beyond 300 pc.

Proposed method

  • Application of Hipparcos astrometric solutions to identify and refine cluster memberships using positional and proper motion data.
  • Computation of mean parallaxes and proper motions for clusters using weighted least-squares fitting of member stars.
  • Use of statistical filtering and consistency checks to exclude non-members and reduce contamination.
  • Evaluation of internal precision and error propagation through Monte Carlo simulations or error analysis.
  • Systematic comparison of derived mean parameters with known cluster distances to detect biases.
  • Incorporation of cluster geometry and radial velocity constraints where available to improve solution stability.

Experimental results

Research questions

  • RQ1What are the most accurate mean parallaxes and proper motions for open clusters within 500 pc using Hipparcos data?
  • RQ2Are there significant systematic biases in the mean parallaxes of open clusters derived from Hipparcos data?
  • RQ3How do the precision levels of Hipparcos-derived astrometric parameters compare to those required for photometric parallax calibration?
  • RQ4Can reliable astrometric solutions be obtained for more distant open clusters beyond 300 pc using Hipparcos data?
  • RQ5What is the impact of membership selection and contamination on the derived mean cluster parameters?

Key findings

  • Mean parallaxes for 18 open clusters within 500 pc were determined with precisions between 0.2 and 0.5 mas.
  • Proper motions were measured with precisions ranging from 0.1 to 0.5 mas/yr, suitable for kinematic studies.
  • No significant systematic errors were found in the mean cluster parallaxes, validating the reliability of the Hipparcos data for cluster analysis.
  • The study provides a robust reference set of astrometric parameters for calibrating photometric parallaxes in the solar neighborhood.
  • Statistical solutions were derived for 32 additional open clusters beyond 300 pc, extending the utility of Hipparcos data to more distant systems.
  • The results demonstrate the feasibility of obtaining high-precision cluster astrometry from a single astrometric catalog, even for moderately distant clusters.

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This review was created by AI and reviewed by human editors.