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As we enter the era of large-scale imaging surveys with the up-comingtelescopes such as LSST and SKA, it is envisaged that the number of knownstrong gravitational lensing systems will increase dramatically. However, theseevents are still very rare and require the efficient processing of millions ofimages. In order to tackle this image processing problem, we present MachineLearning techniques and apply them to the Gravitational Lens Finding Challenge.The Convolutional Neural Networks (CNNs) presented have been re-implementedwithin a new modular, and extendable framework, LEXACTUM. We report an AreaUnder the Curve (AUC) of 0.9343 and 0.9870, and an execution time of 0.0061sand 0.0594s per image, for the Space and Ground datasets respectively, showingthat the results obtained by CNNs are very competitive with conventionalmethods (such as visual inspection and arc finders) for detecting gravitationallenses.

A Comparative Study of Convolutional Neural Networks for the Detection of Strong Gravitational Lensing