Sreevarsha Sreejith
Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning
Sreejith, Sreevarsha; Pereverzyev Jr, Sergiy; Kelvin, Lee S; Marleau, Francine R; Haltmeier, Markus; Ebner, Judith; Bland-Hawthorn, Joss; Driver, Simon P; Graham, Alister W; Holwerda, Benne W; Hopkins, Andrew M; Liske, Jochen; Loveday, Jon; Moffett, Amanda J; Pimbblet, Kevin A; Taylor, Edward N; Wang, Lingyu; Wright, Angus H
Authors
Sergiy Pereverzyev Jr
Lee S Kelvin
Francine R Marleau
Markus Haltmeier
Judith Ebner
Joss Bland-Hawthorn
Simon P Driver
Alister W Graham
Benne W Holwerda
Andrew M Hopkins
Jochen Liske
Jon Loveday
Amanda J Moffett
Professor Kevin Pimbblet K.Pimbblet@hull.ac.uk
Director of DAIM
Edward N Taylor
Lingyu Wang
Angus H Wright
Abstract
© 2018 The Author(s). We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy And Mass Assembly survey to test the feasibility of using automated algorithms to classify galaxies. Using 10 features measured for each galaxy (sizes, colours, shape parameters, and stellar mass), we apply the techniques of Support Vector Machines, Classification Trees, Classification Trees with Random Forest (CTRF) and Neural Networks, and returning True Prediction Ratios (TPRs) of 75.8 per cent, 69.0 per cent, 76.2 per cent, and 76.0 per cent, respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification ('unanimous disagreement') serves as a potential indicator of human error in classification, occurring in ~ 9 per cent of ellipticals, ~ 9 per cent of little blue spheroids, ~ 14 per cent of early-type spirals, ~ 21 per cent of intermediate-type spirals, and ~ 4 per cent of late-type spirals and irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy data sets. Adopting the CTRF algorithm, the TPRs of the five galaxy types are: E, 70.1 per cent; LBS, 75.6 per cent; S0-Sa, 63.6 per cent; Sab-Scd, 56.4 per cent, and Sd-Irr, 88.9 per cent. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS, and S0-Sa) and disc-dominated (Sab-Scd and Sd-Irr), achieving an overall accuracy of 89.8 per cent. This translates into an accuracy of 84.9 per cent for spheroid-dominated systems and 92. 5 per cent for disc-dominated systems.
Citation
Sreejith, S., Pereverzyev Jr, S., Kelvin, L. S., Marleau, F. R., Haltmeier, M., Ebner, J., Bland-Hawthorn, J., Driver, S. P., Graham, A. W., Holwerda, B. W., Hopkins, A. M., Liske, J., Loveday, J., Moffett, A. J., Pimbblet, K. A., Taylor, E. N., Wang, L., & Wright, A. H. (2018). Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning. Monthly notices of the Royal Astronomical Society, 474(4), 5232-5258. https://doi.org/10.1093/mnras/stx2976
Journal Article Type | Article |
---|---|
Acceptance Date | Nov 15, 2017 |
Online Publication Date | Nov 20, 2017 |
Publication Date | Mar 11, 2018 |
Deposit Date | Jun 28, 2018 |
Publicly Available Date | Jul 5, 2018 |
Journal | Monthly Notices of the Royal Astronomical Society |
Print ISSN | 0035-8711 |
Electronic ISSN | 1365-2966 |
Publisher | Oxford University Press |
Peer Reviewed | Peer Reviewed |
Volume | 474 |
Issue | 4 |
Pages | 5232-5258 |
DOI | https://doi.org/10.1093/mnras/stx2976 |
Keywords | Methods: statistical; Galaxies: fundamental parameters; Galaxies: general; Galaxies: structure |
Public URL | https://hull-repository.worktribe.com/output/821779 |
Publisher URL | https://research-repository.st-andrews.ac.uk/handle/10023/12531 |
Contract Date | Jun 28, 2018 |
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Copyright Statement
This article has been accepted for publication in MNRAS ©: 2018 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
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