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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Pith papers citing it
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astro-ph.IM 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Benchmark of Affine, AIM, JetFormer and VQ-VAE tokenizers on galaxy images shows decoupled reconstruction and representation performance with no consistent winner.
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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 Galaxy's Guide to the Tokenizer: A Benchmark for Scientific Foundation Models
Benchmark of Affine, AIM, JetFormer and VQ-VAE tokenizers on galaxy images shows decoupled reconstruction and representation performance with no consistent winner.