Abstract 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 13CO (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 13CO (J = 1−0) line of M16, respectively.
Keywords molecular data – molecular processes – methods: laboratory: molecular
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