Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging
Abstract
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 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 learningmore »
- Authors:
-
- 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
- Publication Date:
- Research Org.:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1605293
- Alternate Identifier(s):
- OSTI ID: 1633752
- Report Number(s):
- arXiv:1908.03610; FERMILAB-PUB-20-236-SCD
Journal ID: ISSN 0035-8711
- Grant/Contract Number:
- AC02-07CH11359
- Resource Type:
- Published Article
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 493 Journal Issue: 3; Journal ID: ISSN 0035-8711
- Publisher:
- Royal Astronomical Society
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS
Citation Formats
Cheng, Ting-Yun, Conselice, Christopher J., Aragón-Salamanca, Alfonso, Li, Nan, Bluck, Asa F. L., Hartley, Will G., Annis, James, Brooks, David, Doel, Peter, García-Bellido, Juan, James, David J., Kuehn, Kyler, Kuropatkin, Nikolay, Smith, Mathew, Sobreira, Flavia, and Tarle, Gregory. Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging. United Kingdom: N. p., 2020.
Web. doi:10.1093/mnras/staa501.
Cheng, Ting-Yun, Conselice, Christopher J., Aragón-Salamanca, Alfonso, Li, Nan, Bluck, Asa F. L., Hartley, Will G., Annis, James, Brooks, David, Doel, Peter, García-Bellido, Juan, James, David J., Kuehn, Kyler, Kuropatkin, Nikolay, Smith, Mathew, Sobreira, Flavia, & Tarle, Gregory. Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging. United Kingdom. https://doi.org/10.1093/mnras/staa501
Cheng, Ting-Yun, Conselice, Christopher J., Aragón-Salamanca, Alfonso, Li, Nan, Bluck, Asa F. L., Hartley, Will G., Annis, James, Brooks, David, Doel, Peter, García-Bellido, Juan, James, David J., Kuehn, Kyler, Kuropatkin, Nikolay, Smith, Mathew, Sobreira, Flavia, and Tarle, Gregory. Wed .
"Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging". United Kingdom. https://doi.org/10.1093/mnras/staa501.
@article{osti_1605293,
title = {Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging},
author = {Cheng, Ting-Yun and Conselice, Christopher J. and Aragón-Salamanca, Alfonso and Li, Nan and Bluck, Asa F. L. and Hartley, Will G. and Annis, James and Brooks, David and Doel, Peter and García-Bellido, Juan and James, David J. and Kuehn, Kyler and Kuropatkin, Nikolay and Smith, Mathew and Sobreira, Flavia and Tarle, Gregory},
abstractNote = {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 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 per cent 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).},
doi = {10.1093/mnras/staa501},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 3,
volume = 493,
place = {United Kingdom},
year = {Wed Feb 19 00:00:00 EST 2020},
month = {Wed Feb 19 00:00:00 EST 2020}
}
https://doi.org/10.1093/mnras/staa501
Web of Science
Figures / Tables:
Works referenced in this record:
Random forest automated supervised classification of Hipparcos periodic variable stars: Classification of Hipparcos periodic stars
journal, May 2011
- Dubath, P.; Rimoldini, L.; Süveges, M.
- Monthly Notices of the Royal Astronomical Society, Vol. 414, Issue 3
Random forests: from early developments to recent advancements
journal, January 2014
- Fawagreh, Khaled; Gaber, Mohamed Medhat; Elyan, Eyad
- Systems Science & Control Engineering, Vol. 2, Issue 1
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
journal, May 2019
- Avestruz, Camille; Li, Nan; Zhu, Hanjue
- The Astrophysical Journal, Vol. 877, Issue 1
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images: II. Quantifying morphological k-correction in the COSMOS field at 1 <
journal, February 2009
- Huertas-Company, M.; Tasca, L.; Rouan, D.
- Astronomy & Astrophysics, Vol. 497, Issue 3
The Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories
journal, July 2003
- Conselice, Christopher J.
- The Astrophysical Journal Supplement Series, Vol. 147, Issue 1
Neocognitron: A neural network model for a mechanism of visual pattern recognition
journal, September 1983
- Fukushima, Kunihiko; Miyake, Sei; Ito, Takayuki
- IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-13, Issue 5
Automated Source Classification Using a Kohonen Network
journal, October 1995
- Mähönen, P. H.; Hakala, P. J.
- The Astrophysical Journal, Vol. 452, Issue 1
The perceptron: A probabilistic model for information storage and organization in the brain.
journal, January 1958
- Rosenblatt, F.
- Psychological Review, Vol. 65, Issue 6
Galaxy Zoo: reproducing galaxy morphologies via machine learning★: Galaxy Zoo: morphology via machine learning
journal, April 2010
- Banerji, Manda; Lahav, Ofer; Lintott, Chris J.
- Monthly Notices of the Royal Astronomical Society, Vol. 406, Issue 1
Star–galaxy separation strategies for WISE-2MASS all-sky infrared galaxy catalogues
journal, February 2015
- Kovács, András; Szapudi, István
- Monthly Notices of the Royal Astronomical Society, Vol. 448, Issue 2
The dark Energy Camera
journal, October 2015
- Flaugher, B.; Diehl, H. T.; Honscheid, K.
- The Astronomical Journal, Vol. 150, Issue 5
Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available Bayesian automated classification
journal, December 2010
- Huertas-Company, M.; Aguerri, J. A. L.; Bernardi, M.
- Astronomy & Astrophysics, Vol. 525
Handwritten Marathi Character Recognition Using R-HOG Feature
journal, January 2015
- Kamble, Parshuram M.; Hegadi, Ravinda S.
- Procedia Computer Science, Vol. 45
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
journal, April 1980
- Fukushima, Kunihiko
- Biological Cybernetics, Vol. 36, Issue 4
Training Products of Experts by Minimizing Contrastive Divergence
journal, August 2002
- Hinton, Geoffrey E.
- Neural Computation, Vol. 14, Issue 8
Improving galaxy morphologies for SDSS with Deep Learning
journal, February 2018
- Domínguez Sánchez, H.; Huertas-Company, M.; Bernardi, M.
- Monthly Notices of the Royal Astronomical Society, Vol. 476, Issue 3
Nearest neighbor pattern classification
journal, January 1967
- Cover, T.; Hart, P.
- IEEE Transactions on Information Theory, Vol. 13, Issue 1, p. 21-27
Deep Learning Identifies High- z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
journal, May 2018
- Huertas-Company, M.; Primack, J. R.; Dekel, A.
- The Astrophysical Journal, Vol. 858, Issue 2
Dark Energy Survey Year 1 Results: The Photometric Data Set for Cosmology
journal, April 2018
- Drlica-Wagner, A.; Sevilla-Noarbe, I.; Rykoff, E. S.
- The Astrophysical Journal Supplement Series, Vol. 235, Issue 2
An automatic taxonomy of galaxy morphology using unsupervised machine learning
journal, September 2017
- Hocking, Alex; Geach, James E.; Sun, Yi
- Monthly Notices of the Royal Astronomical Society, Vol. 473, Issue 1
Galaxy Zoo: the dependence of morphology and colour on environment
journal, March 2009
- Bamford, Steven P.; Nichol, Robert C.; Baldry, Ivan K.
- Monthly Notices of the Royal Astronomical Society, Vol. 393, Issue 4
Automatic morphological classification of galaxy images
journal, November 2009
- Shamir, Lior
- Monthly Notices of the Royal Astronomical Society, Vol. 399, Issue 3
Galaxy types in the Sloan Digital Sky Survey using supervised artificial neural networks
journal, March 2004
- Ball, N. M.; Loveday, J.; Fukugita, M.
- Monthly Notices of the Royal Astronomical Society, Vol. 348, Issue 3
Neural computation as a tool for galaxy classification: methods and examples
journal, October 1996
- Lahav, O.; Nairn, A.; Sodre, L.
- Monthly Notices of the Royal Astronomical Society, Vol. 283, Issue 1
The Dark Energy Survey: Data Release 1
journal, November 2018
- Abbott, T. M. C.; Abdalla, F. B.; Allam, S.
- The Astrophysical Journal Supplement Series, Vol. 239, Issue 2
Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies★: Galaxy Zoo
journal, November 2010
- Lintott, Chris; Schawinski, Kevin; Bamford, Steven
- Monthly Notices of the Royal Astronomical Society, Vol. 410, Issue 1
The strong gravitational lens finding challenge
journal, May 2019
- Metcalf, R. B.; Meneghetti, M.; Avestruz, C.
- Astronomy & Astrophysics, Vol. 625
Cognitron: A self-organizing multilayered neural network
journal, January 1975
- Fukushima, Kunihiko
- Biological Cybernetics, Vol. 20, Issue 3-4
Determining spectroscopic redshifts by using k nearest neighbor regression: I. Description of method and analysis
journal, April 2015
- Kügler, S. D.; Polsterer, K.; Hoecker, M.
- Astronomy & Astrophysics, Vol. 576
Using machine learning to explore the long-term evolution of GRS 1915+105
journal, December 2016
- Huppenkothen, Daniela; Heil, Lucy M.; Hogg, David W.
- Monthly Notices of the Royal Astronomical Society, Vol. 466, Issue 2
Integrating human and machine intelligence in galaxy morphology classification tasks
journal, March 2018
- Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.
- Monthly Notices of the Royal Astronomical Society, Vol. 476, Issue 4
Rotation-invariant convolutional neural networks for galaxy morphology prediction
journal, April 2015
- Dieleman, Sander; Willett, Kyle W.; Dambre, Joni
- Monthly Notices of the Royal Astronomical Society, Vol. 450, Issue 2
Support vector machines and kd-tree for separating quasars from large survey data bases
journal, May 2008
- Gao, Dan; Zhang, Yan-Xia; Zhao, Yong-Heng
- Monthly Notices of the Royal Astronomical Society, Vol. 386, Issue 3
The optimal distance measure for nearest neighbor classification
journal, September 1981
- Short, R.; Fukunaga, K.
- IEEE Transactions on Information Theory, Vol. 27, Issue 5
Learning representations by back-propagating errors
journal, October 1986
- Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J.
- Nature, Vol. 323, Issue 6088
Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification
journal, November 2012
- Zanaty, E. A.
- Egyptian Informatics Journal, Vol. 13, Issue 3
Machine learning and image analysis for morphological galaxy classification
journal, March 2004
- De La Calleja, Jorge; Fuentes, Olac
- Monthly Notices of the Royal Astronomical Society, Vol. 349, Issue 1
Automated morphological classification of APM galaxies by supervised artificial neural networks
journal, August 1995
- Naim, A.; Lahav, O.; Sodre, L.
- Monthly Notices of the Royal Astronomical Society, Vol. 275, Issue 3
A New Approach to Galaxy Morphology. I. Analysis of the Sloan Digital Sky Survey Early Data Release
journal, May 2003
- Abraham, Roberto G.; van den Bergh, Sidney; Nair, Preethi
- The Astrophysical Journal, Vol. 588, Issue 1
Automated Star/Galaxy Classification for Digitized Poss-II
journal, June 1995
- Weir, Nicholas; Fayyad, Usama M.; Djorgovski, S.
- The Astronomical Journal, Vol. 109
An introduction to ROC analysis
journal, June 2006
- Fawcett, Tom
- Pattern Recognition Letters, Vol. 27, Issue 8
Precision spectroscopy of kaonic [sup 3]He X-rays at J-PARC
conference, January 2011
- Sato, M.; Bhang, H.; Cargnelli, M.
- 12TH INTERNATIONAL CONFERENCE ON MESON-NUCLEON PHYSICS AND THE STRUCTURE OF THE NUCLEON (MENU 2010), AIP Conference Proceedings
Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning
journal, November 2017
- Sreejith, Sreevarsha; Pereverzyev Jr, Sergiy; Kelvin, Lee S.
- Monthly Notices of the Royal Astronomical Society, Vol. 474, Issue 4
The use of the area under the ROC curve in the evaluation of machine learning algorithms
journal, July 1997
- Bradley, Andrew P.
- Pattern Recognition, Vol. 30, Issue 7, p. 1145-1159
A Catalog of Visual-Like Morphologies in the 5 Candels Fields Using deep Learning
journal, October 2015
- Huertas-Company, M.; Gravet, R.; Cabrera-Vives, G.
- The Astrophysical Journal Supplement Series, Vol. 221, Issue 1
Gradient-based learning applied to document recognition
journal, January 1998
- Lecun, Y.; Bottou, L.; Bengio, Y.
- Proceedings of the IEEE, Vol. 86, Issue 11
Robust Machine Learning Applied to Astronomical Data Sets. I. Star‐Galaxy Classification of the Sloan Digital Sky Survey DR3 Using Decision Trees
journal, October 2006
- Ball, Nicholas M.; Brunner, Robert J.; Myers, Adam D.
- The Astrophysical Journal, Vol. 650, Issue 1
Automated star/galaxy discrimination with neural networks
journal, January 1992
- Odewahn, S. C.; Stockwell, E. B.; Pennington, R. L.
- The Astronomical Journal, Vol. 103
Restricted Boltzmann machine and softmax regression for fault detection and classification
journal, August 2017
- Chopra, Praveen; Yadav, Sandeep Kumar
- Complex & Intelligent Systems, Vol. 4, Issue 1
Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey
journal, September 2013
- Willett, Kyle W.; Lintott, Chris J.; Bamford, Steven P.
- Monthly Notices of the Royal Astronomical Society, Vol. 435, Issue 4
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images: I. Method description
journal, November 2007
- Huertas-Company, M.; Rouan, D.; Tasca, L.
- Astronomy & Astrophysics, Vol. 478, Issue 3
Morphological Classification of galaxies by Artificial Neural Networks
journal, November 1992
- Storrie-Lombardi, M. C.; Lahav, O.; Sodre, L.
- Monthly Notices of the Royal Astronomical Society, Vol. 259, Issue 1
Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey ★
journal, September 2008
- Lintott, Chris J.; Schawinski, Kevin; Slosar, Anže
- Monthly Notices of the Royal Astronomical Society, Vol. 389, Issue 3
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