PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.
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The NIKA2 survey delivers catalogs of 323 mm-selected sources in COSMOS with redshifts peaking at z=2.8, including 66 at z>4, matching SIDES simulations but inconsistent with four other galaxy evolution models.
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Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS
PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.
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The NIKA2 Cosmological Legacy Survey in COSMOS: Final 1.2mm and 2mm source catalogs and redshift distribution of dusty star-forming galaxies
The NIKA2 survey delivers catalogs of 323 mm-selected sources in COSMOS with redshifts peaking at z=2.8, including 66 at z>4, matching SIDES simulations but inconsistent with four other galaxy evolution models.