FacetClumps: A Facet-based Molecular Clump Detection Algorithm

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm clump detection, called FacetClumps. This uses a morphological approach to extract signal regions from the original data. The Gaussian Facet model employed fit regions, which enhances resistance noise and stability in diverse overlapping areas. introduction extremum determination theorem multivariate functions offers theoretical guidance automatically locating centers. To guarantee that each continuous, are segmented into local based on gradient, then clustered centers connectivity minimum distance identify regional information clump. Experiments conducted with both simulated synthetic data demonstrate FacetClumps exhibits great recall precision rates, small location error flux loss, high consistency between region detected clump, generally stable various environments. Notably, rate data, comprises $^{13}CO$ ($J = 1-0$) emission line MWISP within $11.7^{\circ} \leq l 13.4^{\circ}$, $0.22^{\circ} b 1.05^{\circ}$ 5 km s$^{-1}$ $\leq v \leq$ 35 clumps, reaches 90.2%. Additionally, demonstrates satisfactory performance when applied observational