Cataloging distant Galactic open clusters: Identification of 739 new star cluster candidates beyond 5 kpc utilizing GAIA DR3 data

Abstract Hunting for open clusters (OCs) in Gaia data is a hot topic for astronomical big data analysis. Significant progress has been made in searching for OCs in Gaia data using machine learning methods. Based on our previous research, we applied a hybrid unsupervised clustering algorithm (friends-of-friends and pyUPMASK) and a binary classification algorithm (Random Forests) to perform fine-grained blind searching beyond $5\:$kpc on Gaia DR3. After isochrone-fitting, cross-matching, and visual inspection, we obtained 2932 plausible candidate clusters, 436 of which have been published as candidates in other catalogs. We performed a Kings’s model profile fitting and a comparative study of theoretical tidal radii and the observed mass–radius correlation to determine the physical reality of these OCs. After physical reality checks, we validated the remaining 872 candidate clusters using statistical analysis and dynamical binding distinction. Statistical analysis shows that the distributions of these candidates’ proper motion, OC age, and metallicity are consistent with other related studies. We also analyzed the intrinsic dispersion of morphological features, sky maps, ages, and metallicities of these candidate clusters. Following rigorous verifications and analytical validations, we have ultimately identified 739 OC candidates with high confidence.