Molecular Clump Extraction Algorithm Based on Local Density Clustering*

Luo, Xiaoyu; Zheng, Shibao; Huang, Yao; Zeng, Shu-Guang; Zeng, Xiangyun; Jiang, Zhibo; Chen, Zhiwei

doi:10.1088/1674-4527/ac321d

Cited by 7 publications

(9 citation statements)

References 27 publications

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“…The comprehensive performance of FellWalker, LDC, and ConBased is better than that of GaussClumps, ClumpFind, and ReinHold (Watson 2010;Berry 2015;Li et al 2020;Jiang et al 2022;Luo et al 2022), making them suitable as comparators for our study. The changes in each evaluation metric for different algorithms with respect to S/N are statistically analyzed and presented in Figures 13 and 14.…”

Section: Compare With Other Algorithms In Simulated Clumpsmentioning

confidence: 99%

“…The changes in each evaluation metric for different algorithms with respect to S/N are statistically analyzed and presented in Figures 13 and 14. ΔX and ΔFlux of LDC are based on the direct detections, and the multiple Gaussian model (Luo et al 2022) is not used in this paper.…”

Section: Compare With Other Algorithms In Simulated Clumpsmentioning

confidence: 99%

“…Some of the available clump-finding algorithms include GaussClumps (Stutzki & Guesten 1990), ClumpFind (Williams et al 1994), ReinHold (Berry 2013), FellWalker (Berry 2013), Local Density Clustering (LDC; Luo et al 2022), and ConBased (Jiang et al 2022). The location of peaks is a crucial aspect of GaussClumps, ClumpFind, ReinHold, and LDC.…”

Section: Introductionmentioning

confidence: 99%

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FacetClumps: A Facet-based Molecular Clump Detection Algorithm

Jiang,

Chen,

Zheng

et al. 2023

ApJS

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A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. The experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, and a high consistency between the region of detected clump and that of simulated clump, and the experiments demonstrate that FacetClumps is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises 13CO (J = 1−0) emission line of the MWISP within 11.°7 ≤ l ≤ 13.°4, 0.°22 ≤ b ≤ 1.°05, and 5 km s−1 ≤ v ≤ 35 km s−1 and simulated clumps, reaches 90.2%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.

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Section: Compare With Other Algorithms In Simulated Clumpsmentioning

confidence: 99%

Section: Compare With Other Algorithms In Simulated Clumpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FacetClumps: A Facet-based Molecular Clump Detection Algorithm

Jiang,

Chen,

Zheng

et al. 2023

ApJS

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“…Nevertheless, there are additional variances remaining in the corrected data set. Then we use the Gaussian mixed model estimation method to determine additional variances in the data set after correction (Gao 2020;Luo et al 2022). After several repeated iterations of the algorithm, we can get the different light curves of all the stars in the field of view.…”

Section: Variable Star Detectionmentioning

confidence: 99%

Searching for Variable Stars in the Open Cluster NGC 2355 and Its Surrounding Region

Wang

Zhang

Zeng

et al. 2022

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We have investigated the variable stars in the field surrounding NGC 2355 based on the time-series photometric observation data. More than 3000 CCD frames were obtained in the V band spread over 13 nights with the Nanshan One-meter Wide-field Telescope. We have detected 88 variable stars, containing 72 new variable stars and 16 known variable stars. By analyzing these light curves, we classified the variable stars as follows: 26 eclipsing binaries, 52 pulsating stars, four rotating variables, and six unclear type-variable stars for which their periods are much longer than the time baseline chosen. Employing Gaia DR2 parallax, kinematics, and photometry, the cluster memberships of these variable stars were also analyzed for NGC 2355. In addition to the 11 variable members reported by Cantat-Gaudin et al. (2018), we identify four more variable member candidates located at the outer region of NGC 2355 and showed homogeneity in space positions and kinematic properties with the cluster members. The main physical parameters of NGC 2355 estimated from the two-color and color–magnitude diagrams are log(age/yr) = 8.9, E(B − V) = 0.24 mag, and [Fe/H] = −0.07 dex.

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“…Some of these detection algorithms are based on unsupervised machine learning (e.g. Williams et al 1994;Rosolowsky et al 2008;Berry 2015;Luo et al 2022;Jiang et al 2022Jiang et al , 2023. The ClumpFind algorithm described in Williams et al (1994) has been widely used for the identification of cores and clumps (e.g.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised deep learning for molecular clump verification

Luo,

Zheng,

Jiang

et al. 2024

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A reliable molecular clump detection algorithm is essential for studying these clumps. Existing detection algorithms for molecular clumps still require that detected candidates be verified manually, which is impractical for large-scale data. Semi-supervised learning methods, especially those based on deep features, have the potential to accomplish the task of molecular clump verification thanks to the powerful feature extraction capability of deep networks. Our main objective is to develop an automated method for the verification of molecular clump candidates. This method utilises a 3D convolutional neural network (3D CNN) to extract features of molecular clumps and employs semi-supervised learning to train the model, with the aim being to improve its generalisation ability and data utilisation. It addresses the issue of insufficient labelled samples in traditional supervised learning and enables the model to better adapt to new, unlabelled samples, achieving high accuracy in the verification of molecular clumps. We propose SS-3D-Clump, a semi-supervised deep clustering method that jointly learns the parameters of a 3D CNN and the cluster assignments of the generated features for automatic verification of molecular clumps. SS-3D-Clump iteratively classifies the features with the Constrained-KMeans and uses these class labels as supervision to update the weights of the entire network. We used CO data from the Milky Way Imaging Scroll Painting project covering 350 square degrees in the Milky Way’s first, second, and third quadrants. The ClumpFind algorithm was applied to extract molecular clump candidates in these regions, which were subsequently verified using SS-3D-Clump. The SS-3D-Clump model, trained on a dataset comprising three different density regions, achieved an accuracy of 0.933, a recall rate of 0.955, a precision rate of 0.945, and an F1 score of 0.950 on the corresponding test dataset. These results closely align with those obtained through manual verification. Our experiments demonstrate that the SS-3D-Clump model achieves high accuracy in the automated verification of molecular clumps. It effectively captures the essential features of the molecular clumps and overcomes the challenge of limited labelled samples in supervised learning by using unlabelled samples through semi-supervised learning. This enhancement significantly improves the generalisation capability of the SS-3D-Clump model, allowing it to adapt effectively to new and unlabelled samples. Consequently, SS-3D-Clump can be integrated with any detection algorithm to create a comprehensive framework for the automated detection and verification of molecular clumps.

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Molecular Clump Extraction Algorithm Based on Local Density Clustering*

Cited by 7 publications

References 27 publications

FacetClumps: A Facet-based Molecular Clump Detection Algorithm

FacetClumps: A Facet-based Molecular Clump Detection Algorithm

Searching for Variable Stars in the Open Cluster NGC 2355 and Its Surrounding Region

Semi-supervised deep learning for molecular clump verification

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