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The Kinematics of the Young Stellar Population in the W5 Region of the Cassiopeia OB6 Association: Implication for the Formation Process of Stellar Associations

Lim et al. (2023)

임범두 #1 Lead 홍종석 #2 Co-author 이진희 #3 Co-author 윤형식 #4 Co-author Narae Hwang #5 Co-author PARK, BYEONG GON #6 Co-author
THE ASTRONOMICAL JOURNAL IF 5.491 A 등급
MMT(Multi-Mirror Telescope)

Abstract

The star-forming region W5 is a major part of the Cassiopeia OB6 association. Its internal structure and kinematics may provide hints of the star formation process in this region. Here, we present a kinematic study of young stars in W5 using the Gaia data and our radial velocity data. A total 490 out of 2000 young stars are confirmed as members. Their spatial distribution shows that W5 is highly substructured. We identify a total of eight groups using the k-means clustering algorithm. There are three dense groups in the cavities of H ii bubbles, and the other five sparse groups are distributed at the edges of the bubbles. The three dense groups have almost the same age (5 Myr) and show a pattern of expansion. The scale of their expansion is not large enough to account for the overall structure of W5. The three northern groups are, in fact, 3 Myr younger than the dense groups, which indicates independent star formation events. Only one of these groups shows the signature of feedback-driven star formation as its members move away from the eastern dense group. The other two groups might have formed in a spontaneous way. On the other hand, the properties of two southern groups are not understood as those of a coeval population. Their origins can be explained by dynamical ejection of stars and multiple star formation. Our results suggest that the substructures in W5 formed through multiple star-forming events in a giant molecular cloud.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

classification / clustering

supporting

Techniques

classical-ML

Tasks

clustering/unsupervised

Subfield

stars

tabular-catalog

Flags

Develops AI method
· Classification confidence: 100%