A denoising VAE-GCNN pipeline applied to JWST/NIRCam images yields a 0.34 fraction of disk-like galaxies at 4 < z < 7.7.
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Extension of an existing deep-learning pipeline to JWST data identifies 382 disk-like galaxies among 1380 massive systems at 0.5 < z < 4, indicating such morphologies persist to high redshift.
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Disk-like galaxies at 4 < z < 7.7 : JWST/NIRCam morphologies revealed by denoising VAE-GCNN classification
A denoising VAE-GCNN pipeline applied to JWST/NIRCam images yields a 0.34 fraction of disk-like galaxies at 4 < z < 7.7.
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Enhancing Galaxy Classification with U-Net Variational Autoencoders. III. Disk-like Galaxy Identification in JWST Samples of up to redshift 4
Extension of an existing deep-learning pipeline to JWST data identifies 382 disk-like galaxies among 1380 massive systems at 0.5 < z < 4, indicating such morphologies persist to high redshift.
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