Exploring Galactic open clusters with Gaia I. An examination in the first kiloparsec

TL;DR

This study uses Gaia DR3 data and advanced clustering techniques to identify and characterize stellar members in 370 nearby open clusters, revealing more faint stars and refining cluster parameters with improved accuracy.

Contribution

It introduces a combined astrometric and photometric clustering method using HDBSCAN and Mahalanobis metric to improve membership identification in open clusters.

Findings

Identified about 40% more stars in clusters compared to previous Gaia DR2 studies.

Recovered up to 10% more faint stars using photometry in clustering.

Provided refined estimates of age, metallicity, and extinction for the clusters.

Abstract

Context. Since the first publication of the Gaia catalogue a new view of our Galaxy has arrived. Its astrometric and photometric information has improved the precision of the physical parameters of open star clusters obtained from them. Aims. Using the Gaia DR3 catalogue, we aim to find physical stellar members including faint stars for 370 Galactic open clusters located within 1 kpc. We also estimate the age, metallicity, distance modulus and extinction of these clusters. Methods. We employ the HDBSCAN algorithm on both astrometric and photometric data to identify members in the open clusters. Subsequently, we refine the samples by eliminating outliers through the application of the Mahalanobis metric utilizing the chi-square distribution at a confidence level of 95%. Furthermore, we characterize the stellar parameters with the PARSEC isochrones. Results. We obtain reliable star members for 370 open clusters with an average parallax error of 0.16 mas. We identify about 40% more stars in these clusters compared to previous work using the Gaia DR2 catalogue, including faint stars as new members with G > 17. Before the clustering application we correct the parallax zero-point bias to avoid spatial distribution stretching that may affect clustering results. Our membership lists include merging stars identified by HDBSCAN with astrometry and photometry. We note that the use of photometry in clustering can recover up to 10% more stars in the fainter limit than clustering based on astrometry only, this combined with the selection of stars filtering them out by quality cuts significantly reduces the number of stars with huge parallax error. After clustering, we estimate age, Z, and AV from the photometry of the membership lists.