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The Zone of Avoidance makes it difficult for astronomers to cataloguegalaxies at low latitudes to our galactic plane due to high star densities andextinction. However, having a complete sky map of galaxies is important in anumber of fields of research in astronomy. There are many unclassified sourcesof light in the Zone of Avoidance and it is therefore important that thereexists an accurate automated system to identify and classify galaxies in thisregion. This study aims to evaluate the efficiency and accuracy of using anevolutionary algorithm to evolve the topology and configuration ofConvolutional Neural Network (CNNs) to automatically identify galaxies in theZone of Avoidance. A supervised learning method is used with data containingnear-infrared images. Input image resolution and number of near-infraredpassbands needed by the evolutionary algorithm is also analyzed while theaccuracy of the best evolved CNN is compared to other CNN variants.

Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance