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| Paper: |
Object Classification with Convolutional Neural Networks: from KiDS to Euclid |
| Volume: |
538, ADASS XXXII |
| Page: |
122 |
| Authors: |
Kleijn, G. A. Verdoes; Marocico, C. A.; Mzayek, Y.; Pöntinen, M.; Granvik, M.; Williams, O.; Jong, J. T. A. de; Saifollahi, T.; Wang, L.; Margalef-Bentabol, B.; Marca, A. La; Nagam, B. Chowdhary; Koopmans, L. V. E.; Valentijn, E. A. |
| DOI: |
10.26624/OHEN8831 |
| Abstract: |
Large-scale imaging surveys have grown ∼1000 times faster than the
number of astronomers in the last 3 decades. Using Artificial Intelligence instead of
astronomer’s brains for interpretative tasks allows astronomers to keep up with the data.
We give a progress report on using Convolutional Neural Networks (CNNs) to classify
three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids)
in the Kilo-Degree Survey (KiDS) and the Euclid Survey. |
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