WaveDiff with wavefront feature projection recovers WFE from noisy undersampled in-focus observations at ~3% error, a tenfold improvement over the prior version.
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astro-ph.IM 2years
2026 2verdicts
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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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Point spread function wavefront recovery from in-focus stellar observations
WaveDiff with wavefront feature projection recovers WFE from noisy undersampled in-focus observations at ~3% error, a tenfold improvement over the prior version.
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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.