Assessing the performance of molecular gas clump identification algorithms

The detection of clumps (cores) in molecular clouds is an important issue in sub-millimetre astronomy. However, the completeness of the identification and the accuracy of the returned parameters of the automated clump identification algorithms are still not clear. 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 signal-to-noise ratio (SNRs) 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 together, 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.