Open clusters (OCs) are regarded as tracers to understand stellar evolution theory and validate stellar models. In this study, we presented a robust approach to identifying OCs. A hybrid method consisting of pyUPMASK (Pera+ 2021A&A...650A.109P) and the random forest (RF) algorithm is first used to remove field stars and determine more reliable members. An identification model based on the RF algorithm built based on 3714 OC samples from Gaia DR2 and EDR3 is then applied to identify OC candidates. The OC candidates are obtained after isochrone fitting, advanced stellar population synthesis model fitting, and visual inspection. Using the proposed approach, we revisited 868 candidates and preliminarily clustered them by the friends-of-friends algorithm in Gaia EDR3. Excluding OCs that have already been reported, we focused on the remaining 300 unknown candidates. From high to low fitting quality, these unrevealed candidates were further classified into Class A (59), Class B (21), and Class C (220). As a result, 46 new reliable OC candidates among Classes A and B are identified after visual inspection.

Cone search capability for table J/ApJS/265/20/table2 (Parameters of the final 46 new open clusters (OCs) in this work)