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

Research output: Contribution to journalArticleAcademicpeer-review

9 Downloads (Pure)

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.
Original languageEnglish
Number of pages7
JournalArXiv
Publication statusSubmitted - 3-Mar-2024

Keywords

  • Astrophysics - Instrumentation and Methods for Astrophysics

Fingerprint

Dive into the research topics of 'Object classification with Convolutional Neural Networks: from KiDS to Euclid'. Together they form a unique fingerprint.

Cite this