Molecular Clump Extraction Algorithm Based on Local Density Clustering

TL;DR

This paper introduces a novel algorithm combining Local Density Clustering and Multiple Gaussian Model techniques for accurate detection and characterization of molecular clumps in astronomical data, demonstrating high accuracy and flux recovery.

Contribution

The paper presents a new method integrating Local Density Clustering with Gaussian modeling for improved molecular clump detection and parameter estimation.

Findings

Achieved 90.2% flux recovery rate in M16 data.

Detection rate of 81.7% for molecular clumps.

High accuracy under varying signal-to-noise conditions.

Abstract

The detection and parametrization of molecular clumps is the first step in studying them. We propose a method based on 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 be derived reliably. Using simulation and synthetic data, we have verified that the proposed algorithm could characterize the morphology and flux of molecular clumps accurately. The total flux recovery rate in $^{13}\rm CO$ (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 $^{13}\rm CO$ (J=1-0) line of M16, respectively.