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Deep learning for studies of galaxy morphology

  • D. Tuccillo (a1) (a2), M. Huertas-Company (a1), E. Decencière (a2) and S. Velasco-Forero (a2)

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, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.

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Bastien F., et al., (2012), NIPS, 2012
Dieleman S., Willett K. W., & Dambre J. 2015, MNRAS, 450, 1441
Fukushima K. 1980, Biological Cybernetics, 36, 193202
Galametz A., Grazian A., Fontana A., et al. 2013, ApJS, 206, 10
Huertas-Company M., et al. 2015, ApJS, 221, 8
Kim E. & Brunner R. 2017, MNRAS, 464, 44634475
Koekemoer A. M., et al. 2013, ApJS, 209, 3
Krizhevsky A., Sutskever I., & Hinton G. 2012, NIPS1097–1105
LeCun Y., Boser B., Denker J. S., Henderson D., Howard R. E., Hubbard W., & Jackel L. D. 1989, Neural Computation, 1 (4):541551
Peng C. Y., et al. 2002, AJ, 124, 266
Schmidhuber J. 2015, Neural Netw, 61, 85117
van der Wel A., et al. (2012) ApJS, 203, 24
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Proceedings of the International Astronomical Union
  • ISSN: 1743-9213
  • EISSN: 1743-9221
  • URL: /core/journals/proceedings-of-the-international-astronomical-union
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