Wednesday

As the scale of data continues to increase with upcoming surveys, traditional galaxy morphology classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold, classifying 226,124 galaxies in 92 days of GZ2 project time with 95.7% accuracy as compared to labels derived from GZ2 classification data.

We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system
provides a factor of 11.4 increase in the classification rate, classifying 210,543 galaxies in just 32 days of GZ2 project time with 93.5% accuracy. These results have important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.