LDC-MGM

Molecular Clump extraction algorithm based on Local Density Clustering

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.

Note The core idea of the algorithm comes from this paper

Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492.

Dependencies

The code is completed with Python 3. The following dependencies are needed to run the code:

• numpy~=1.19.2
• pandas~=1.3.2
• tabulate~=0.8.9
• matplotlib~=3.3.4
• scikit-image~=0.18.1
• scipy~=1.6.2
• astropy~=4.2

Install

I suggest you install the code using pip from an Anaconda Python 3 environment. From that environment:

git clone https://github.com/Luoxiaoyu828/LDC-MGM.git
cd LDC-MGM/dist
pip install DensityClust-***.tar.gz

or you can install LDC package directly in pypi.com. using:

pip install DensityClust

Usage

import package

import astropy.io.fits as fits
from tools.make_plot import make_plot
import LDC_MGM.LDC_MGM_main as ldc_mgm
import LDC_MGM.LDC_main as ldc
from DensityClust.localDenst2 import Param

setting params & filename

data_name = r'example_data\3d_Clumps\gaussian_out_000.fits'
para = Param(delta_min=4, gradmin=0.01, v_min=[25, 5], noise_times=3, rms_times=5, res=[30, 30, 0.166], dc='auto',
                 data_rms_path='', rms_key='RMS', rms=rms_value, id_prefix='MWISP')
para.rm_touch_edge = False
save_folder = r'temp'

How to set the parameters of algorithm, you can use the command

from DensityClust.localDenst2 import Param
help(Param)

LDC

ldc.LDC_main(data_name, para, save_folder)

LDC MGM

save_mgm_png = False
ldc_mgm.LDC_MGM_main(data_name, para, save_folder, split=False, save_mgm_png=save_mgm_png)

make picture

data = fits.getdata(r'example_data\3d_Clumps\gaussian_out_000.fits')
outcat = r'***.csv'
make_plot.make_plot(outcat, data, lable_num=False)

Citation

If you use this code in a scientific publication, I would appreciate citation/reference to this repository.

@ARTICLE{2022RAA....22a5003L,
       author = {{Luo}, Xiaoyu and {Zheng}, Sheng and {Huang}, Yao and {Zeng}, Shuguang and {Zeng}, Xiangyun and {Jiang}, Zhibo and {Chen}, Zhiwei},
        title = "{Molecular Clump Extraction Algorithm Based on Local Density Clustering}",
      journal = {Research in Astronomy and Astrophysics},
     keywords = {molecular data, molecular processes, methods: laboratory: molecular, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2022,
        month = jan,
       volume = {22},
       number = {1},
          eid = {015003},
        pages = {015003},
          doi = {10.1088/1674-4527/ac321d},
archivePrefix = {arXiv},
       eprint = {2110.11620},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022RAA....22a5003L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}