  We present a machine learning (ML) pipeline to identify star clusters in the
multi{color images of nearby galaxies, from observations obtained with the
Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy
ExtraGalactic Ultraviolet Survey). StarcNet (STAR Cluster classification
NETwork) is a multi-scale convolutional neural network (CNN) which achieves an
accuracy of 68.6% (4 classes)/86.0% (2 classes: cluster/non-cluster) for star
cluster classification in the images of the LEGUS galaxies, nearly matching
human expert performance. We test the performance of StarcNet by applying
pre-trained CNN model to galaxies not included in the training set, finding
accuracies similar to the reference one. We test the effect of StarcNet
predictions on the inferred cluster properties by comparing multi-color
luminosity functions and mass-age plots from catalogs produced by StarcNet and
by human-labeling; distributions in luminosity, color, and physical
characteristics of star clusters are similar for the human and ML classified
samples. There are two advantages to the ML approach: (1) reproducibility of
the classifications: the ML algorithm's biases are fixed and can be measured
for subsequent analysis; and (2) speed of classification: the algorithm
requires minutes for tasks that humans require weeks to months to perform. By
achieving comparable accuracy to human classifiers, StarcNet will enable
extending classifications to a larger number of candidate samples than
currently available, thus increasing significantly the statistics for cluster
studies.
