FacetClumps: A Facet-based Molecular Clump Detection Algorithm

Abstract

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. The experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, and a high consistency between the region of detected clump and that of simulated clump, and the experiments demonstrate that FacetClumps is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises 13CO (J = 1−0) emission line of the MWISP within 11.°7 ≤ l ≤ 13.°4, 0.°22 ≤ b ≤ 1.°05, and 5 km s−1 ≤ v ≤ 35 km s−1 and simulated clumps, reaches 90.2%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.

A Catalog of 13CO Clumps from the MWISP in l = 10°–20°

“…Then, local regions of the centers are clustered to identify clumps by considering connectivity and minimum distance. The experiments (Jiang et al 2023) show that FacetClumps exhibits excellent recall and precision rates.…”

“…FacetClumps 7 was developed by Jiang et al (2023), which uses the Gaussian facet model to fit local surfaces and employs the extremum determination theorem of multivariate functions to determine clump centers. Based on the identified clump centers, FacetClumps clusters the regions near the centers by considering connectivity and minimum distance, thereby obtaining and detecting molecular clumps.…”

“…Based on the identified clump centers, FacetClumps clusters the regions near the centers by considering connectivity and minimum distance, thereby obtaining and detecting molecular clumps. The FacetClumps algorithm uses default parameters in this paper (see Appendix C in Jiang et al 2023). By cross-matching the detection results of the two algorithms, "extremely" high-confidence clumps can obtained.…”

“…With the progress of sky survey projects, unsupervised machine learning-based algorithms for detecting molecular clumps have also emerged (e.g., Rosolowsky et al 2008;Berry 2015;Luo et al 2022;Jiang et al 2023). The dendrogram algorithm, developed by Rosolowsky et al (2008), is well-suited for illustrating changes in the hierarchical structure of isosurfaces within molecular line data cubes as contour levels vary (Rani et al 2023).…”

“…Additionally, a Multiple Gaussian Model (MGM) is utilized to simultaneously fit overlapping clumps, allowing for the extraction of clump parameters. Jiang et al (2023) presented FacetClumps, a Gaussian facet-based model designed for fitting local surfaces. The extremum determination theorem of multivariate functions is applied to determine clump centers.…”