Galaxy Morphology Classification with Deep Convolutional Neural Networks

We propose a variant of residual networks (ResNets) for galaxy morphology classification. The variant, together with other popular convolutional neural networks (CNNs), are applied to a sample of 28790 galaxy images from Galaxy Zoo 2 dataset, to classify galaxies into five classes, i.e. completely r...

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Veröffentlicht in:	arXiv.org 2018-07
Hauptverfasser:	Jia-Ming, Dai, Tong, Jizhou
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Artificial neural networks Cigars Classification Galaxies Image classification Morphology Neural networks Physics - Astrophysics of Galaxies Physics - Instrumentation and Methods for Astrophysics
Online-Zugang:	Volltext
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