Gaia Early Data Release 3: The Gaia Catalogue of Nearby Stars
Gaia Collaboration, R.L. Smart, L.M. Sarro, J. Rybizki, C. Reyl\'e,, A.C. Robin, N.C. Hambly, U. Abbas, M.A. Barstow, J.H.J. de Bruijne, B., Bucciarelli, J.M. Carrasco, W.J. Cooper, S.T. Hodgkin, E. Masana, D., Michalik, J. Sahlmann, A. Sozzetti, A.G.A. Brown, A. Vallenari

TL;DR
This paper presents a carefully curated catalogue of nearby stars within 100 parsecs from Gaia EDR3, demonstrating its scientific potential through detailed analysis of stellar populations, clusters, and kinematic structures.
Contribution
The authors developed a novel machine-learning based method to identify and characterize stars within 100 pc using Gaia EDR3 data, including Bayesian distance estimation and contamination assessment.
Findings
Catalogue contains at least 92% of M9 stars within 100 pc.
Identified 12 members of Gaia Enceladus stream.
First direct parallaxes for five systems within 10 pc.
Abstract
We produce a clean and well-characterised catalogue of objects within 100\,pc of the Sun from the \G\ Early Data Release 3. We characterise the catalogue through comparisons to the full data release, external catalogues, and simulations. We carry out a first analysis of the science that is possible with this sample to demonstrate its potential and best practices for its use. The selection of objects within 100\,pc from the full catalogue used selected training sets, machine-learning procedures, astrometric quantities, and solution quality indicators to determine a probability that the astrometric solution is reliable. The training set construction exploited the astrometric data, quality flags, and external photometry. For all candidates we calculated distance posterior probability densities using Bayesian procedures and mock catalogues to define priors. Any object with reliable…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
