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A catalog of 13CO Clumps from FUGIN in the Milky Way: l = 10◦ − 50◦

Zheng, Sheng, Pan, Xuejiao, Urquhart, J.S., Luo, Xiaoyu, Huang, Yao, Jiang, Zhibo, Chen, Zhiwei, Zeng, Shuguang, Zeng, Xianggyun, Zhang, Junjie and others. (2025) A catalog of 13CO Clumps from FUGIN in the Milky Way: l = 10◦ − 50◦. Astronomy & Astrophysics, . ISSN 0004-6361. (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:112401)

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Abstract

Context. Since stars and star clusters emerge from the gravitational collapse of the clumps and cores, studying molecular clumps is fundamental to understanding star formation. The FUGIN (FOREST Unbiased Galactic Plane Imaging) survey offers insights into the distribution of clumps and physical properties across different environments, aiding in studying environmental effects such as location within the galaxy on star formation.

Aims. This study aims to produce a catalog of clumps from the FUGIN survey to understand the complete mechanism of high-mass star formation in giant molecular clouds (GMCs). We use the catalog to analyze the physical properties of clumps in high-mass star-forming regions, enhancing our understanding of how different environments impact the star-formation process.

Methods. The detection and verification of 13CO clumps in the FUGIN survey involves two steps. First, the source extraction code FacetClumps is used to detect as many molecular clump candidates as possible from the FUGIN 13CO data. Second, a trained and validated semi-supervised deep learning model, SS-3D-Clump, is applied to verify these candidates, providing confidence levels for the clumps and filtering out false candidates to enhance the accuracy of the detection results.

Results. The resulting catalog containing 23,150 clumps extracted from the 13CO (J = 1 − 0) data covers the first quadrant (10◦ ≤ l ≤ 50◦, |b| ≤ 1◦). By matching with CHIMPS and inheriting the distances of the matched CHIMPS clumps, we found that the sizes of the FUGIN clumps range from 0.1 to 3 pc, showing that the dense structures belong to the clump scale. The catalog achieves an 80% completeness level above 466 K km s−1.

Conclusions. The proposed two-step approach effectively integrates clump detection algorithms with semi-supervised deep learning, achieving an accuracy comparable to manual verification and thereby improving the extraction of clumps from large-scale survey data. The resulting clump catalog enables the analysis of the physical properties of clumps in high-mass star-forming regions, contributing to a better understanding of the environmental influences on clump formation and the star formation process

Item Type: Article
Additional information: For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising
Uncontrolled keywords: Interstellar molecules – Molecular clumps – Catalog – methods: Deep Learning
Subjects: Q Science > QB Astronomy > QB460 Astrophysics
Institutional Unit: Schools > School of Engineering, Mathematics and Physics > Physics and Astronomy
Former Institutional Unit:
There are no former institutional units.
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: James Urquhart
Date Deposited: 16 Dec 2025 15:09 UTC
Last Modified: 17 Dec 2025 09:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/112401 (The current URI for this page, for reference purposes)

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