Identifying cluster members of NGC 869 and NGC 884 using a combined machine learning method

Abstract This work describes a method for calculating cluster membership probability from big, high dimensional newly released GAIA-EDR3 observations that combine the Gaussian Mixture Model (GMM) with Random Forest (RF). technique has considerable advantage of rapidly identifying possible members in big data sets using modest training samples. The result indicates double clusters NGC 869 20270 points ultra-high (p≥ 0.8) probable members, 9294 are 0.6) and 6181 stars moderate (0.2 <= p < members. 884 21317, 10871 5998. Cluster centers were also determined to be at α = 2h 22m 17s, δ 57h 8m 60s 19m 2s, 7m 48s respectively. mean proper motion -0.690 ± 12 mas/yr, or -1.150 16 parallax 0.410 04 mas. old member was quantified 621, new 1129. 1511, up previous 920 We found combined GMM RF effective observations.