Hierarchical Structure and Self-gravity in the Maddalena Giant Molecular Cloud
Renjie Shen, Yuehui Ma, Hongchi Wang, Suziye He, Miaomiao Zhang

TL;DR
This study analyzes the hierarchical substructures of the Maddalena GMC using 13CO data, revealing how self-gravity influences their properties and binding status across different spatial scales.
Contribution
It introduces a modified Dendrogram algorithm to decompose hierarchical structures in GMCs and compares their properties in star-forming and quiescent regions.
Findings
Most substructures in star-forming regions are gravitationally bound.
Self-gravity significantly affects structures on 0.8-4 pc scales in star-forming areas.
Hierarchical analysis reveals different gravitational states compared to non-hierarchical methods.
Abstract
In this work, we present the data from the Milky Way Imaging Scroll Painting (MWISP) project for the Maddalena giant molecular cloud (GMC). We decompose the 13CO emission datacube of the observed region into hierarchical substructures using a modified Dendrogram algorithm. We investigate the statistical properties of these substructures and examine the role that self-gravity plays on various spatial scales. The statistics of the mass (M), radius (R), velocity dispersion ({\sigma}v), virial parameter ({\alpha}vir), and sonic Mach number of the substructures are presented. The radius and mass distributions and the {\sigma}v-R scaling relationship of the substructures resemble those reported in previous studies that use non-hierarchical algorithms to identify the entities. We find that for the hierarchical substructures {\alpha}vir decreases as the radius or mass of the substructures…
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Taxonomy
TopicsCosmology and Gravitation Theories · Astrophysics and Star Formation Studies · Relativity and Gravitational Theory
