BSEC method for unveiling open clusters and its application to Gaia DR3: 83 new clusters
Zhongmu Li, Caiyan Mao

TL;DR
The paper introduces the BSEC method, combining machine learning techniques and a novel color excess constraint, to efficiently discover and accurately characterize open clusters in Gaia DR3 data, revealing 83 new clusters and 621 candidates.
Contribution
It presents the BSEC approach, integrating HDBSCAN, GMM, and a new color excess constraint, for improved open cluster detection and membership determination in large stellar datasets.
Findings
Discovered 83 new open clusters in Gaia DR3 data.
Identified 621 new open cluster candidates.
Enhanced cluster member accuracy using the color excess constraint.
Abstract
Open clusters (OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques (e.g., machine-learning algorithms) available for the exploration of OCs. However, each method has its limitations and therefore, different approaches to discovering OCs hold significant value. We develop a comprehensive approach method to automatically explore the data space and identify potential OC candidates with relatively reliable membership determination. This approach combines the techniques of HDBSCAN, GMM, and a novel cluster member identification technique, color excess constraint. The new method exhibits efficiency in detecting OCs while ensuring precise determination of cluster memberships. It is called Blind Search-Extra Constraint (BSEC) method. It is successfully applied to the Gaia DR3, and 83 new OCs are found. This study also reports 621 new OC…
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Taxonomy
TopicsRegional Development and Policy · Distributed and Parallel Computing Systems · Cognitive Science and Mapping
