Star Cluster Classification in the PHANGS-HST Survey: Comparison between
  Human and Machine Learning Approaches
  When completed, the PHANGS-HST project will provide a census of roughly
50,000 compact star clusters and associations, as well as human morphological
classifications for roughly 20,000 of those objects. These large numbers
motivated the development of a more objective and repeatable method to help
perform source classifications. In this paper we consider the results for five
PHANGS-HST galaxies (NGC 628, NGC 1433, NGC 1566, NGC 3351, NGC 3627) using
classifications from two convolutional neural network architectures (RESNET and
VGG) trained using deep transfer learning techniques. The results are compared
to classifications performed by humans. The primary result is that the neural
network classifications are comparable in quality to the human classifications
with typical agreement around 70 to 80$\%$ for Class 1 clusters (symmetric,
centrally concentrated) and 40 to 70$\%$ for Class 2 clusters (asymmetric,
centrally concentrated). If Class 1 and 2 are considered together the agreement
is 82 $\pm$ 3$\%$. Dependencies on magnitudes, crowding, and background surface
brightness are examined. A detailed description of the criteria and methodology
used for the human classifications is included along with an examination of
systematic differences between PHANGS-HST and LEGUS. The distribution of data
points in a colour-colour diagram is used as a 'figure of merit' to further
test the relative performances of the different methods. The effects on science
results (e.g., determinations of mass and age functions) of using different
cluster classification methods are examined and found to be minimal.
