Evaluating the feasibility of interpretable machine learning for
  globular cluster detection
  Extragalactic globular clusters (GCs) are important tracers of galaxy
formation and evolution. Obtaining GC catalogues from photometric data involves
several steps which will likely become too time-consuming to perform on the
large data volumes that are expected from upcoming wide-field imaging projects
such as Euclid. In this work, we explore the feasibility of various machine
learning (ML) methods to aid the search for GCs. We use archival Hubble Space
Telescope data in the F475W and F850LP bands of 141 early-type galaxies in the
Fornax and Virgo galaxy clusters. Using existing GC catalogues to label the
data, we obtain an extensive data set of 84929 sources containing 18556 GCs and
we train several ML methods both on image and tabular data containing
physically relevant features extracted from the images. We find that our
evaluated ML models are capable of producing catalogues of similar quality as
the existing ones. The best performing methods, ensemble-based models like
random forests and convolutional neural networks, recover ~ 90-94 % of GCs
while producing an acceptable amount of false detections (~ 6-8 %) - with some
falsely detected sources being identifiable as GCs that have not been labelled
as such in the used catalogues. In the magnitude range 22 < m4_g < 24.5 mag, 98
- 99 % of GCs are recovered. We even find such high performance levels when
training on Virgo and evaluating on Fornax data (and vice versa), illustrating
that the models are transferable to environments with different conditions such
as different distances than in the used training data. Additionally, we
demonstrate how interpretable methods can be used to better understand model
predictions, recovering that magnitudes, colours, and sizes are important for
identifying GCs. These are encouraging results, indicating that similar methods
can be applied for creating GC catalogues for a large number of galaxies.
