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There are several supervised machine learning methods used for theapplication of automated morphological classification of galaxies; however,there has not yet been a clear comparison of these different methods usingimaging data, or a investigation for maximising their effectiveness. We carryout a comparison between several common machine learning methods for galaxyclassification (Convolutional Neural Network (CNN), K-nearest neighbour,Logistic Regression, Support Vector Machine, Random Forest, and NeuralNetworks) by using Dark Energy Survey (DES) data combined with visualclassifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determinethe optimal machine learning methods when using imaging data for galaxyclassification. We show that CNN is the most successful method of these tenmethods in our study. Using a sample of $\sim$2,800 galaxies with visualclassification from GZ1, we reach an accuracy of $\sim$0.99 for themorphological classification of Ellipticals and Spirals. The furtherinvestigation of the galaxies that have a different ML and visualclassification but with high predicted probabilities in our CNN usually revealsan the incorrect classification provided by GZ1. We further find the galaxieshaving a low probability of being either spirals or ellipticals are visuallyLenticulars (S0), demonstrating that supervised learning is able to rediscoverthat this class of galaxy is distinct from both Es and Spirals. We confirm that$\sim$2.5\% galaxies are misclassified by GZ1 in our study. After correctingthese galaxies' labels, we improve our CNN performance to an average accuracyof over 0.99 (accuracy of 0.994 is our best result).

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging