Abstract
In this paper, a deep learning-based approach has been developed to classify the images of galaxies into three major categories, namely, elliptical, spiral, and irregular. The classifier successfully classified the images with an accuracy of 97.3958%, which outperformed conventional classifiers like Support Vector Machine and Naive Bayes. The convolutional neural network architecture involves one input convolution layer having 16 filters, followed by 4 hidden layers, 1 penultimate dense layer, and an output Softmax layer. The model was trained on 4614 images for 200 epochs using NVIDIA-DGX-1 Tesla-V100 Supercomputer machine and was subsequently tested on new images to evaluate its robustness and accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Odewahn, S.C., Stockwell, E.B., Pennington, R.L., Humphreys, R.M., Zumach, W.A.: Automated star/galaxy discrimination with neural networks. In: Digitised Optical Sky Surveys, pp. 215–224. Springer (1992)
De Vaucouleurs, G.: Classification and morphology of external galaxies. In: Astrophysik iv: Sternsysteme/Astrophysics iv: Stellar Systems, pp. 275–310. Springer (1959)
Hupp, E., Roy, S., Watzke, M.: NASA finds direct proof of dark matter. Press Release (2006)
Basu, A., Mandal, A., Das, A.: Blueprint of the graphical galaxy of solar system segment: an algorithmic approach using CSS3.0
Liller, M.H.: The distribution of intensity in elliptical galaxies of the virgo cluster ii. Astrophys. J. 146, 28 (1966)
Nieto, J.-L., Capaccioli, M., Held, E.V.: More isotropic oblate rotators in elliptical galaxies. Astron. Astrophys. 195, L1–L4 (1988)
Mihalas, D., Binney, J.: Galactic astronomy* wh freeman co. San Francisco (1968)
Benjamin, R.A., Churchwell, E., Babler, B.L., Indebetouw, R., Meade, M.R., Whitney, B.A., Watson, C., Wolfire, M.G., Wolff, M.J., Ignace, R., et al.: First glimpse results on the stellar structure of the galaxy. Astrophys. J. Lett. 630(2), L149 (2005)
Grebel, E.K.: The evolutionary history of local group irregular galaxies. In: Origin and Evolution of the Elements, p. 234 (2004)
Kormendy, J., Bender, R.: A revised parallel-sequence morphological classification of galaxies: structure and formation of s0 and spheroidal galaxies. Astrophys. J. Supp. Ser. 198(1), 2 (2011)
Buta, R.J., Sheth, K., Athanassoula, E., Bosma, A., Knapen, J.H., Laurikainen, E., Salo, H., Elmegreen, D., Ho, L.C., Zaritsky, D., et al.: A classical morphological analysis of galaxies in the spitzer survey of stellar structure in galaxies (S4G). Astrophys. J. Supp. Ser. 217(2), 32 (2015)
Shamir, L.: Automatic morphological classification of galaxy images. Mon. Not. R. Astron. Soc. 399(3), 1367–1372 (2009)
Selim, I.M., Keshk, A.E., El Shourbugy, B.M.: Galaxy image classification using non-negative matrix factorization. Int. J. Comput. Appl. 137(5) (2016)
AstronomÃa, M., Cordero Garayar, J.P., Campusano Brown, L.E., Blanc Mendiberri, G., De Propris, R., Muñoz Vidal, R.: The dry merger rate and merger relic fraction in the coma cluster core
Selim, I.M., Abd El Aziz, M.: Automated morphological classification of galaxies based on projection gradient nonnegative matrix factorization algorithm. Exp. Astron. 43(2), 131–144 (2017)
Kim, E.J., Brunner, R.J.: Star-galaxy classification using deep convolutional neural networks. Mon. Not. R. Astron. Soc. 2672 (2016)
Van Dyk, S.D., Peng, C.Y., Barth, A.J., Filippenko, A.V.: The environments of supernovae in post-refurbishment hubble space telescope images. Astron. J. 118(5), 2331 (1999)
Haykin, S.: Network, neural: a comprehensive foundation. Neural Netw. 2(2004), 41 (2004)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). arXiv:1511.06434
De La Calleja, J., Fuentes, O.: Machine learning and image analysis for morphological galaxy classification. Mon. Not. R. Astron. Soc. 349(1), 87–93 (2004)
Angelica Marin, M., Enrique Sucar, L., Gonzalez, J.A., Diaz, R.: A hierarchical model for morphological galaxy classification. In: FLAIRS Conference (2013)
Khalifa, N.E.M., Taha, M.H.N., Ella Hassanien, A., Selim, I.M.: Deep galaxy: classification of galaxies based on deep convolutional neural networks (2017). arXiv:1709.02245
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Misra, D., Mohanty, S.N., Agarwal, M., Gupta, S.K. (2020). Convoluted Cosmos: Classifying Galaxy Images Using Deep Learning. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-32-9949-8_40
Download citation
DOI: https://doi.org/10.1007/978-981-32-9949-8_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9948-1
Online ISBN: 978-981-32-9949-8
eBook Packages: EngineeringEngineering (R0)