A rules-based and Transfer Learning approach for deriving the Hubble type of a galaxy from the Galaxy Zoo data

The Galaxy Zoo project is a crowd-sourced astronomy galaxy classification endeavour whose results can have significant benefits to astronomers. The project has evolved into using crowd-sourced labelling together with machine learning to automate the classification of galaxies. If this process is to be automated using crowd-sourcing and machine learning, then understanding how these results will hold up against expert classifications on an academically accepted classification such as the Hubble tuning fork is timely. We propose a rules-based approach for deriving the Hubble type using the responses in the Galaxy Zoo as well as a Transfer Learning approach for solving this problem. The dataset we used to get the Hubble type for galaxies is the Revised Shapley-Ames catalogue of bright galaxies. Previous work in this field has mainly revolved around the Galaxy Zoo project with little to no attempt to map the Galaxy Zoo responses to a more robust method of classifying galaxies such as the Hubble tuning fork classification system. Previous research has tried to map the Galaxy Zoo responses to a set of classes like elliptical, spiral and irregular. Their work has shown promising results. Our experiments showed that by using the Galaxy Zoo response vectors, our rules-based approach was able to separate the elliptical and spiral shapes, however, it did not perform particularly well at separating the spiral shapes from one another. Our Transfer Learning model showed better potential for separating not only elliptical and spiral shapes but also for separating spiral shapes into exact Hubble types (e.g