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Object classification with Convolutional Neural Networks: from KiDS to Euclid

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Kleijn, G. A. Verdoes, Marocico, C. A., Mzayek, Y., Pöntinen, M., Granvik, M., Williams, O., de Jong, J. T. A., Saifollahi, T., Wang, L., Margalef-Bentabol, B., La Marca, A., Nagam, B. Chowdhary, Koopmans, L. V. E., Valentijn, E. A.
Format:	Preprint
Publié:	2024
Sujets:	Instrumentation and Methods for Astrophysics
Accès en ligne:	Acceder al recurso
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author	Kleijn, G. A. Verdoes Marocico, C. A. Mzayek, Y. Pöntinen, M. Granvik, M. Williams, O. de Jong, J. T. A. Saifollahi, T. Wang, L. Margalef-Bentabol, B. La Marca, A. Nagam, B. Chowdhary Koopmans, L. V. E. Valentijn, E. A.
author_facet	Kleijn, G. A. Verdoes Marocico, C. A. Mzayek, Y. Pöntinen, M. Granvik, M. Williams, O. de Jong, J. T. A. Saifollahi, T. Wang, L. Margalef-Bentabol, B. La Marca, A. Nagam, B. Chowdhary Koopmans, L. V. E. Valentijn, E. A.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_01613
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Object classification with Convolutional Neural Networks: from KiDS to Euclid Kleijn, G. A. Verdoes Marocico, C. A. Mzayek, Y. Pöntinen, M. Granvik, M. Williams, O. de Jong, J. T. A. Saifollahi, T. Wang, L. Margalef-Bentabol, B. La Marca, A. Nagam, B. Chowdhary Koopmans, L. V. E. Valentijn, E. A. Instrumentation and Methods for Astrophysics 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.
title	Object classification with Convolutional Neural Networks: from KiDS to Euclid
topic	Instrumentation and Methods for Astrophysics
url	https://arxiv.org/abs/2403.01613