Version 1
: Received: 16 June 2018 / Approved: 18 June 2018 / Online: 18 June 2018 (16:12:10 CEST)
Version 2
: Received: 19 October 2018 / Approved: 22 October 2018 / Online: 22 October 2018 (13:01:42 CEST)
Silva, P.; Cao, L.T.; Hayes, W.B. SpArcFiRe: Enhancing Spiral Galaxy Recognition Using Arm Analysis and Random Forests. Galaxies2018, 6, 95.
Silva, P.; Cao, L.T.; Hayes, W.B. SpArcFiRe: Enhancing Spiral Galaxy Recognition Using Arm Analysis and Random Forests. Galaxies 2018, 6, 95.
Silva, P.; Cao, L.T.; Hayes, W.B. SpArcFiRe: Enhancing Spiral Galaxy Recognition Using Arm Analysis and Random Forests. Galaxies2018, 6, 95.
Silva, P.; Cao, L.T.; Hayes, W.B. SpArcFiRe: Enhancing Spiral Galaxy Recognition Using Arm Analysis and Random Forests. Galaxies 2018, 6, 95.
Abstract
Automated machine classifications of galaxies are necessary because the size of upcoming surveys will overwhelm human volunteers. We improve upon existing machine classification methods by adding the output of SpArcFiRe to the inputs of a machine learning model. We use the human classifications from Galaxy Zoo 1 (GZ1) to train a random forest of decision trees to reproduce the human vote distributions of the Spiral class. We prefer the random forest model over other black box models like neural networks because it allows us to trace post hoc the precise reasoning behind the classification of each galaxy. We find that, across a sample of 470,000 Sloan galaxies that are large enough that details could be seen if they were there, the combination of SpArcFiRe outputs with existing SDSS features provides a better machine classification than either one alone on comparison to Galaxy Zoo 1. We suggest that adding SpArcFiRe outputs as features to any machine learning algorithm will likely improve its performance.
Keywords
galaxy morphology, machine learning; data analysis; object classification
Subject
Physical Sciences, Astronomy and Astrophysics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
