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2017

Deep learning for studies of galaxy morphology

Tuccillo, D., Huertas-Company, M., Decenciere, E. et al.

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Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge.

Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach.
Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks.
In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sersic indices, total flux etc..).

Deep learning for studies of galaxy morphology · Around

Built on

Fukushima, K. 1980, Biological Cybernetics
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LeCun,Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., Jackel, L. D. 1989, Neural Computation
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Peng, C. Y. et al. 2002, AJ
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Bastien F. et al., (2012), NIPS
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Then

Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS
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Later among the works it cites.
Huertas-Company M., et al. 2015, ApJS
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Kim, E. & Brunner, R. 2017, MNRAS
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