Dwarfs from the Dark (Energy Survey): a machine learning approach to
  classify dwarf galaxies from multi-band images
  Countless low-surface brightness objects - including spiral galaxies, dwarf
galaxies, and noise patterns - have been detected in recent large surveys.
Classically, astronomers visually inspect those detections to distinguish
between real low-surface brightness galaxies and artefacts. Employing the Dark
Energy Survey (DES) and machine learning techniques, Tanoglidis et al. (2020)
have shown how this task can be automatically performed by computers. Here, we
build upon their pioneering work and further separate the detected low-surface
brightness galaxies into spirals, dwarf ellipticals, and dwarf irregular
galaxies. For this purpose, we have manually classified 5567 detections from
multi-band images from DES and searched for a neural network architecture
capable of this task. Employing a hyperparameter search, we find a family of
convolutional neural networks achieving similar results as with the manual
classification, with an accuracy of 85% for spiral galaxies, 94% for dwarf
ellipticals, and 52% for dwarf irregulars. For dwarf irregulars - due to their
diversity in morphology - the task is difficult for humans and machines alike.
Our simple architecture shows that machine learning can reduce the workload of
astronomers studying large data sets by orders of magnitudes, as will be
available in the near future with missions such as Euclid.
