Mining Hubble Space Telescope Images

Astrophysicists rely on crowd sourced initiatives to classify galaxies in large surveys. The next generation of telescopes will lead to a revolutionary increase in the amount of unlabelled data available to astrophysicists making crowd sourcing infeasible. To cope with this significant increase in data astrophysicists will need unsupervised techniques. In this paper we show that a model using unsupervised learning can automatically categorise galaxies in Hubble Space Telescope survey images. We discover the optimum parameter combination using a grid search like approach. We also present a detailed analysis of the effect of varying parameters. We analyse Hubble Space Telescope images and evaluate the model's performance for identifying major categories of galaxies and for identifying a type of lensed galaxy. We find that the pixel intensity power spectrum is the most effective at identifying elliptical, spiral and background galaxies whereas the rotationally invariant feature transform is more effective at detecting a type of lensed galaxy that occurs as a result of strong lensing.