Integrating human and machine intelligence in galaxy morphology classification tasks. Issue 4 (6th March 2018)

• Record Type: Journal Article
• Authors: Beck, Melanie R; Scarlata, Claudia; Fortson, Lucy F; Lintott, Chris J; Simmons, B D; Galloway, Melanie A; Willett, Kyle W; Dickinson, Hugh; Masters, Karen L; Marshall, Philip J; Wright, Darryl
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 476:Issue 4(2018)
• Page Start: 5516
• Page End: 5534
• Publication Date: 2018-03-06
• Subjects: methods: data analysis -- methods: statistical -- galaxies: statistics -- galaxies: structure; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/sty503
• Languages: English
• ISSNs: 0035-8711

Abstract

Abstract: Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional 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 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. 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 at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.