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Title:
Integrating human and machine intelligence in galaxy morphology classification tasks
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
Affiliation:
AA(Minnesota Institute for Astrophysics, University of Minnesota, Minneapolis, MN 55455, USA 0000-0002-8826-1381), AB(Minnesota Institute for Astrophysics, University of Minnesota, Minneapolis, MN 55455, USA), AC(Minnesota Institute for Astrophysics, University of Minnesota, Minneapolis, MN 55455, USA), AD(Oxford Astrophysics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK; New College, Oxford OX1 3BN, UK), AE(Oxford Astrophysics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK; Center for Astrophysics and Space Sciences, Department of Physics, University of California, San Diego, CA 92093, USA), AF(Minnesota Institute for Astrophysics, University of Minnesota, Minneapolis, MN 55455, USA), AG(Minnesota Institute for Astrophysics, University of Minnesota, Minneapolis, MN 55455, USA), AH(Minnesota Institute for Astrophysics, University of Minnesota, Minneapolis, MN 55455, USA), AI(Institute of Cosmology and Gravitation, Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX UK), AJ(Kavli Institute for Particle Astrophysics and Cosmology, P.O. Box 20450, MS29, Stanford, CA 94309, USA), AK(Oxford Astrophysics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK)
Publication:
Monthly Notices of the Royal Astronomical Society, Volume 476, Issue 4, p.5516-5534 (MNRAS Homepage)
Publication Date:
06/2018
Origin:
OUP
Astronomy Keywords:
methods: data analysis, methods: statistical, galaxies: statistics, galaxies: structure
Abstract Copyright:
2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society
DOI:
10.1093/mnras/sty503
Bibliographic Code:
2018MNRAS.476.5516B

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.
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