Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging
[1];
[9];
- School of Physics and Astronomy, The University of Nottingham, University Park, Nottingham NG7 2RD, UK
- Kavli Institute for Cosmology, The University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
- Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK;Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland
- Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA
- Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK
- Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, E-28049 Madrid, Spain
- Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
- Australian Astronomical Optics, Macquarie University, North Ryde NSW 2113, Australia
- School of Physics and Astronomy, University of Southampton, Southampton SO17 1BJ, UK
- Instituto de Física Gleb Wataghin, Universidade Estadual de Campinas, 13083-859, Campinas, SP, Brazil;Laboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ-20921-400, Brazil
- Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
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 an investigation for maximizing 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 optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1648538
- Report Number(s):
- FERMILAB-PUB-20-236-SCD; oai:inspirehep.net:1812528
- Journal Information:
- Monthly Notices of the Royal Astronomical Society, Vol. 493, Issue 3; ISSN 0035-8711
- Publisher:
- Royal Astronomical Society
- Country of Publication:
- United States
- Language:
- English
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