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

Object classification with Convolutional Neural Networks: from KiDS to Euclid

Instrumentation and Methods for Astrophysics 2024-03-05 v1

Authors: 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.

Keywords

machine learning in astronomy

Cite

@article{arxiv.2403.01613,
  title  = {Object classification with Convolutional Neural Networks: from KiDS to Euclid},
  author = {G. A. Verdoes Kleijn and C. A. Marocico and Y. Mzayek and M. Pöntinen and M. Granvik and O. Williams and J. T. A. de Jong and T. Saifollahi and L. Wang and B. Margalef-Bentabol and A. La Marca and B. Chowdhary Nagam and L. V. E. Koopmans and E. A. Valentijn},
  journal= {arXiv preprint arXiv:2403.01613},
  year   = {2024}
}

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ADASS XXXII - 2022, Victoria, Conference Proceedings

Object classification with Convolutional Neural Networks: from KiDS to Euclid

Abstract

Keywords

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