Galaxy And Mass Assembly : automatic morphological classification of galaxies using statistical learning
MetadataShow full item record
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.
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 , vol. 474 , no. 4 , pp. 5232-5258 . https://doi.org/10.1093/mnras/stx2976
Monthly Notices of the Royal Astronomical Society
© 2017, the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at https://doi.org/10.1093/mnras/stx2976
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Showing items related by title, author, creator and subject.
Mendez Abreu, Jairo; Debattista, V. P.; Corsini, E. M.; Aguerri, J. A. L. (2014-12) - Journal articleContext. Historically, galaxy bulges were thought to be single-component objects at the center of galaxies. However, this picture is now questioned since different bulge types with different formation paths, namely classical ...
Galaxy And Mass Assembly (GAMA) : the wavelength-dependent sizes and profiles of galaxies revealed by MegaMorph Vulcani, Benedetta; Bamford, Steven P.; Haeussler, Boris; Vika, Marina; Rojas, Alex; Agius, Nicola K.; Baldry, Ivan; Bauer, Amanda E.; Brown, Michael J. I.; Driver, Simon; Graham, Alister W.; Kelvin, Lee S.; Liske, Jochen; Loveday, Jon; Popescu, Cristina C.; Robotham, Aaron S. G.; Tuffs, Richard J. (2014-06) - Journal articleWe investigate the relationship between colour and structure within galaxies using a large, volume-limited sample of bright, low-redshift galaxies with optical-near-infrared imaging from the Galaxy And Mass Assembly survey. ...
The UV continua and inferred stellar populations of galaxies at z ≃ 7-9 revealed by the Hubble Ultra-Deep Field 2012 campaign Dunlop, J. S.; Rogers, A. B.; McLure, R. J.; Ellis, R. S.; Robertson, B. E.; Koekemoer, A.; Dayal, P.; Curtis-Lake, E.; Wild, Vivienne; Charlot, S.; Bowler, R. A. A.; Schenker, M. A.; Ouchi, M.; Ono, Y.; Cirasuolo, M.; Furlanetto, S. R.; Stark, D. P.; Targett, T. A.; Schneider, E. (2013-07-11) - Journal articleWe use the new ultra-deep, near-infrared imaging of the Hubble Ultra-Deep Field (HUDF) provided by our UDF12 Hubble Space Telescope (HST) Wide Field Camera 3/IR campaign to explore the rest-frame ultraviolet (UV) properties ...