Object classification with Convolutional Neural Networks: from KiDS to Euclid

G. A. Verdoes Kleijn; C. A. Marocico; Y. Mzayek; M. Pöntinen; M. Granvik; O. Williams; J. T. A. de Jong; T. Saifollahi; L. Wang; B. Margalef-Bentabol; A. La Marca; B. Chowdhary Nagam; L. V. E. Koopmans; E. A. Valentijn

arXiv · 2403.01613

Object classification with Convolutional Neural Networks: from KiDS to Euclid

G. A. Verdoes Kleijn·C. A. Marocico·Y. Mzayek·M. Pöntinen·M. Granvik·O. Williams·J. T. A. de Jong·T. Saifollahi·L. Wang·B. Margalef-Bentabol·A. La Marca·B. Chowdhary Nagam·L. V. E. Koopmans·E. A. Valentijn

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Abstract

Large-scale imaging surveys have grown about 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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G. A. Verdoes Kleijn, C. A. Marocico, Y. Mzayek, M. Pöntinen, M. Granvik, O. Williams, J. T. A. de Jong, T. Saifollahi, L. Wang, B. Margalef-Bentabol, A. La Marca, B. Chowdhary Nagam, L. V. E. Koopmans, E. A. Valentijn. 2024-03-03. Object classification with Convolutional Neural Networks: from KiDS to Euclid. https://arxiv.org/abs/2403.01613

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