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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Models with prominent TP-AGB phases best fit the near-IR spectra of high-redshift quiescent galaxies, yielding younger ages and lower stellar masses than models with weaker TP-AGB contributions.
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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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Prevailing thermally-pulsing-asymptotic-giant branch stars in the near-infrared rest-frame spectra of distant quiescent galaxies: towards robust galaxy ages and masses
Models with prominent TP-AGB phases best fit the near-IR spectra of high-redshift quiescent galaxies, yielding younger ages and lower stellar masses than models with weaker TP-AGB contributions.