Identify 46 New Open Clusters Candidates In Gaia EDR3 Using pyUPMASK and Random Forest Hybrid Method

TL;DR

This paper introduces a hybrid pyUPMASK and Random Forest method to identify new open cluster candidates in Gaia EDR3, successfully discovering 46 previously unknown clusters through a combination of machine learning, isochrone fitting, and visual inspection.

Contribution

The study presents a novel hybrid approach combining pyUPMASK and RF algorithms for more reliable open cluster identification in Gaia data, leading to the discovery of 46 new candidates.

Findings

Identified 46 new open cluster candidates in Gaia EDR3.

Developed a hybrid pyUPMASK and RF method for cluster detection.

Classified candidates into quality categories based on fitting results.

Abstract

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 of pyUPMASK and RF 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, the advanced stellar population synthesis (ASPS) 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 the open clusters 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), respectively. As a result, 46 new reliable open cluster candidates among classes A and B are identified after visual inspection.