Assessing the performance of molecular gas clump identification algorithms

Abstract

The detection of clumps(cores) in molecular clouds is an important issue in submillimetre astronomy. However, the completeness of the identification and the accuracy of the returned parameters of the automated clump identification algorithms are still not clear by now. In this work, we test the performance and bias of the GaussClumps, ClumpFind, Fellwalker, Reinhold, and Dendrograms algorithms in identifying simulated clumps. By designing the simulated clumps with various sizes, peak brightness, and crowdedness, we investigate the characteristics of the algorithms and their performance. In the aspect of detection completeness, Fellwalker, Dendrograms, and Gaussclumps are the first, second, and third best algorithms, respectively. The numbers of correct identifications of the six algorithms gradually increase as the size and SNR of the simulated clumps increase and they decrease as the crowdedness increases. In the aspect of the accuracy of retrieved parameters, Fellwalker and Dendrograms exhibit better performance than the other algorithms. The average deviations in clump parameters for all algorithms gradually increase as the size and SNR of clumps increase. Most of the algorithms except Fellwalker exhibit significant deviation in extracting the total flux of clumps. Taken altogether, Fellwalker, Gaussclumps, and Dendrograms exhibit the best performance in detection completeness and extracting parameters. The deviation in virial parameter for the six algorithms is relatively low. When applying the six algorithms to the clump identification for the Rosette molecular cloud, ClumpFind1994, ClumpFind2006, Gaussclumps, Fellwalker, and Reinhold exhibit performance that is consistent with the results from the simulated test.

An Unbiased CO Survey Toward the Northern Region of the Small Magellanic Cloud with the Atacama Compact Array. II. CO Cloud Catalog

“…Therefore, it is useful to treat the large outer and small inner structures separately. The dendrogram algorithm astrodendro (Rosolowsky et al 2008;Shetty et al 2012;Colombo et al 2015) is one of the best options to meet our requirements (see also the comparison of different cloud-decomposition methods by Li et al 2020). Several studies (Wong et al 2017;Naslim et al 2018;Nayak et al 2018;Wong et al 2019) applied the same scheme to ALMA CO data of molecular clouds in the Large Magellanic Cloud (LMC) at an angular resolution of ∼1 pc.…”

The molecular gas resolved by ALMA in the low-metallicity merging dwarf galaxy Haro 11

“…We used the Python package Astrodendro 3 (Goodman et al 2009) to search for star-forming clumps in the Hα map. This clumpfinding algorithm is based on the dendrogram and has been used to identify reliable star-forming cores in galaxies (see Li et al 2020 for the detailed comparison of different clump-finding packages). To define the boundaries of clump structures, we can specify a few parameters: min_value, min_delta and min_npix.…”

An Unbiased CO Survey toward the Northern Region of the Small Magellanic Cloud with the Atacama Compact Array. I. Overview: CO Cloud Distributions

“…Therefore, it is useful to treat the large outer and small inner structures separately. The dendrogram algorithm, astrodendro (Rosolowsky et al 2008;Shetty et al 2012;Colombo et al 2015), is one of the best options to meet our requirements (see also a comparison of different cloud-decomposition methods by Li et al 2020). Several studies (Wong et al 2017(Wong et al , 2019Nayak et al 2018;Naslim et al 2018) applied the same scheme to ALMA CO data of molecular clouds in the Large Magellanic Cloud (LMC) at an angular resolution of ∼1 pc.…”