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An Investigation of the Pleiades Cluster Using Machine Learning
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Journal Article
An Investigation of the Pleiades Cluster Using Machine Learning
2019
Overview
This paper presents an investigation on fundamental astrophysical properties of the Pleiades cluster (M 45) using high-precision astrometric and photometric data from the Gaia Data Release 2 (Gaia-DR2). To obtain reliable cluster members, a machine-learning (ML) method is used to compute membership probabilities for 31462 sample stars within a radius of 6.5° from the cluster center, both the astrometric and photometric data are taken into account. We obtain a total number of 1454 likely cluster members with membership probabilities larger than 0.6, including a well-known white dwarf (LB 1497) with a high membership probability of ∼0.96. We find a well-defined relationship between the parallaxes and proper motions of the cluster members, the most likely explanation for the relationship is that the depth effect of the cluster along the line of sight must be taken into consideration. Using Monte Carlo simulations, the most likely distance, proper motion, and radial velocity of the cluster are determined to be D = 136.0 0.1 pc, ( 〈 cos δ 〉 , 〈 δ 〉 ) = (+20.141 0.093, −45.536 0.081) mas yr−1, and 〈 V r 〉 = + 5.8 0.1 km s − 1 , respectively. It is found that the likely cluster members extend outward to a limiting radius of Rlim = 310′ 12′ (12.3 0.5 pc) from the cluster center, and the total mass of the cluster within this radius is Mtot = 721 93 M . We find clear evidence for the presence of spatial mass segregation in this young cluster by analyzing the photometry and spatial positions of the likely cluster members. Interestingly, we also find that four high-mass cluster members with high membership probabilities (>0.99) are being ejected from the inner region of the cluster, they may have formed via close encounters between single and binary stars.
Publisher
The Astronomical Society of the Pacific,IOP Publishing
