학술논문
Object classification with Convolutional Neural Networks: from KiDS to Euclid
Document Type
Working Paper
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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.
Comment: ADASS XXXII - 2022, Victoria, Conference Proceedings
Comment: ADASS XXXII - 2022, Victoria, Conference Proceedings