publication . Preprint . Article . 2019

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging

Ting-Yun Cheng; Christopher J. Conselice; Alfonso Aragón-Salamanca; Nan Li; Asa F. L. Bluck; W. G. Hartley; James Annis; David J. Brooks; Peter Doel; Juan Garcia-Bellido; ...
Open Access English
  • Published: 09 Aug 2019
Abstract
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation for maximising their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification (Convolutional Neural Network (CNN), K-nearest neighbour, Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks) by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the op...
Persistent Identifiers
Subjects
arXiv: Astrophysics::Cosmology and Extragalactic AstrophysicsAstrophysics::Galaxy Astrophysics
free text keywords: Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Space and Planetary Science, Astronomy and Astrophysics, Random forest, Machine learning, computer.software_genre, computer, Deep learning, Galaxy, Support vector machine, Convolutional neural network, Physics, Artificial neural network, Dark energy, Supervised learning, Artificial intelligence, business.industry, business
Funded by
EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
,
NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
,
EC| TESTDE
Project
TESTDE
Testing the Dark Energy Paradigm and Measuring Neutrino Mass with the Dark Energy Survey
  • Funder: European Commission (EC)
  • Project Code: 291329
  • Funding stream: FP7 | SP2 | ERC
,
EC| COGS
Project
COGS
Capitalizing on Gravitational Shear
  • Funder: European Commission (EC)
  • Project Code: 240672
  • Funding stream: FP7 | SP2 | ERC
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