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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
The Causes Of The Red Sequence, The Blue Cloud, The Green Valley, And The Green Mountain, Stephen A. Eales, Maarten Baes, Nathan Bourne, Malcolm Bremer, Michael J.I. Brown, Christopher Clark, David Clements, Pieter De Vis, Simon Driver, Loretta Dunne, Simon Dye, Cristina Furlanetto, Benne W. Holwerda, R. J. Ivison, L. S. Kelvin, Maritza Lara-Lopez, Lerothodi Leeuw, Jon Loveday, Steve Maddox, Michal J. Michalowski, Steven Phillipps, Aaron Robotham, Dan Smith, Matthew Smith, Elisabetta Valiante, Paul Van Der Werf, Angus Wright
The Causes Of The Red Sequence, The Blue Cloud, The Green Valley, And The Green Mountain, Stephen A. Eales, Maarten Baes, Nathan Bourne, Malcolm Bremer, Michael J.I. Brown, Christopher Clark, David Clements, Pieter De Vis, Simon Driver, Loretta Dunne, Simon Dye, Cristina Furlanetto, Benne W. Holwerda, R. J. Ivison, L. S. Kelvin, Maritza Lara-Lopez, Lerothodi Leeuw, Jon Loveday, Steve Maddox, Michal J. Michalowski, Steven Phillipps, Aaron Robotham, Dan Smith, Matthew Smith, Elisabetta Valiante, Paul Van Der Werf, Angus Wright
Faculty Scholarship
The galaxies found in optical surveys fall in two distinct regions of a diagram of optical colour versus absolute magnitude: the red sequence and the blue cloud, with the green valley in between. We show that the galaxies found in a submillimetre survey have almost the opposite distribution in this diagram, forming a 'green mountain'. We show that these distinctive distributions follow naturally from a single, continuous, curved Galaxy Sequence in a diagram of specific star formation rate versus stellar mass, without there being the need for a separate star-forming galaxy main sequence and region of passive galaxies. The cause …
Galaxy And Mass Assembly: Automatic Morphological Classification Of Galaxies Using Statistical Learning, Sreevarsha Sreejith, Sergiy Pereverzyev, 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, Kevin A. Pimbblet, Edward N. Taylor, Lingyu Wang, Angus H. Wright
Galaxy And Mass Assembly: Automatic Morphological Classification Of Galaxies Using Statistical Learning, Sreevarsha Sreejith, Sergiy Pereverzyev, 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, Kevin A. Pimbblet, Edward N. Taylor, Lingyu Wang, Angus H. Wright
Faculty Scholarship
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.