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 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.
