Molecular Clump Extraction Algorithm Based on Local Density Clustering*

The detection and parameterization of molecular clumps are the first step in studying them. We propose a method based on the Local Density Clustering algorithm while physical parameters of those clumps are measured using the Multiple Gaussian Model algorithm. One advantage of applying the Local Density Clustering to the clump detection and segmentation, is the high accuracy under different signal-to-noise levels. The Multiple Gaussian Model is able to deal with overlapping clumps whose parameters can reliably be derived. Using simulation and synthetic data, we have verified that the proposed algorithm could accurately characterize the morphology and flux of molecular clumps. The total flux recovery rate in (CO)-C-13 (J = 1-0) line of M16 is measured as 90.2%. The detection rate and the completeness limit are 81.7% and 20 K km s(-1) in (CO)-C-13 (J = 1-0) line of M16, respectively.

Automated summary

In this study, a method based on the Local Density Clustering algorithm and the Multiple Gaussian Model algorithm is proposed to accurately detect and parameterize molecular clumps, especially under different signal-to-noise levels. The accuracy of the algorithm has been verified using simulation and synthetic data.