Catalog of ¹³CO clumps from FUGIN in the Milky Way at l = 10°–50°

¹ Center for Astronomy and Space Sciences, China Three Gorges University, Yichang 443000, China
² Centre for Astrophysics and Planetary Science, University of Knet, Canterbury CT2 7NH, UK
³ Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023, China
⁴ College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443000, China

^★ Corresponding authors: This email address is being protected from spambots. You need JavaScript enabled to view it. ; This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 18 September 2024
Accepted: 10 December 2025

Abstract

Context. Since stars and star clusters emerge from the gravitational collapse of clumps and cores, studying molecular clumps is fundamental to understanding the processes of star formation. The FOREST Unbiased Galactic Plane Imaging (FUGIN) survey offers insights into the distribution of clumps and physical properties across different environments, aiding in studies of environmental effects, such as the 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. Our process for the detection and verification of ¹³CO clumps in the FUGIN survey comprised two steps. First, the source extraction code FacetClumps was 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, was 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, demonstrating 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 environmental influences on clump formation and the star formation process.