A tool of Hierarchical cOre ideNtification and Kinematic property AssIgnment (HONKAI) for Dense Cores
Jiawei Liu, Zhiyuan Ren, Di Li, Jinjin Xie, Gary A. Fuller, Yuchen Xing, Xin Lyu, Fengwei Xu, Chen Wang, Fanyi Meng, Sihan Jiao

TL;DR
This paper introduces HONKAI, an automated tool for hierarchical structure identification and kinematic analysis in IRDCs, enabling detailed cataloging of cores and their properties from multi-band data.
Contribution
HONKAI is a novel automated procedure that resolves cores, disentangles velocity components, measures physical properties, and generates comprehensive catalogs for IRDCs.
Findings
193 dense cores identified in 16 clumps.
Majority of cores have virial ratio > 1, indicating self-gravitation.
Core mass function shows a steep slope at high masses.
Abstract
Infrared dark clouds (IRDCs) contains cold dense gas at the earliest stage of massive star and cluster formation. In studying the IRDCs, a universal and fundamental task is to resolve their internal hierarchical structures. Various packages and algorithms were developed for this purpose, but with most of them mainly focused on certain individual steps in data processing. In this work, we build a more automatic procedure for multi-band structure measurement HONKAI (Hierarchical cOre ideNtification and Kinematic property AssIgnment), which can resolve the elemental components including cores and clumps, disentangle the velocity components in spectral data, measure their physical properties, and generate a catalogue for all the measured properties. We use {\sc honkai} for a joint study towards three IRDCs observed in 850 m dust continuum with James Clerk Maxwell Telescope (JCMT) and…
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