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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4 Pith papers cite this work. Polarity classification is still indexing.
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JWST observations show larger average rest-UV than rest-optical sizes in z=1.5-3 galaxies, supporting inside-out disk formation after dust correction.
Rest-frame 6-8um MIRI luminosity provides broken power-law SFR calibrations with 0.2-0.3 dex scatter and UV+IR composites at 0.15 dex, supporting robust use above log M* ~9 up to z~3.
citing papers explorer
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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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Quantifying the inside-out formation of disk galaxies at $1.5 \le z \le 3.0$
JWST observations show larger average rest-UV than rest-optical sizes in z=1.5-3 galaxies, supporting inside-out disk formation after dust correction.
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Calibrating Photometric Mid-Infrared Star Formation Rates for JWST
Rest-frame 6-8um MIRI luminosity provides broken power-law SFR calibrations with 0.2-0.3 dex scatter and UV+IR composites at 0.15 dex, supporting robust use above log M* ~9 up to z~3.
- The Fraction of Clumpy Galaxies in JADES Over $2<z<9$