Short Summary of What we did
To classify galaxies morphologically, we developed Galaxy Morphology Network,a convolutional neural network that classifies galaxies according to their bulge-to-total ratio. GaMorNet does not need a large training set of real data and can be applied to datasets with a range of signal-to-noise ratios and spatial resolutions. We first trained GaMorNet on simulations of galaxies with a bulge and a disk component and then used a technique called transfer learning to refine the already trained network using $\sim25\%$ of the real dataset to achieve net misclassification rates of $\lesssim5\%$. This has very important consequences, as the applicability of CNNs to future data-intensive surveys like LSST, WFIRST, and Euclid will depend on their ability to perform on multiple data-sets without the need for a large training set of real data.