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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Multi-element Bayesian modeling of 23 EELGs reveals short depletion timescales and large mass-loading factors in a burst-driven regime, with abundance ratios isolating star-formation efficiency, outflows, and inflows.
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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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Unraveling Chemical Enrichment in Extreme Emission-Line Galaxies: A Multi-Element Bayesian View of Bursty Star Formation and Galaxy Evolution in DESI
Multi-element Bayesian modeling of 23 EELGs reveals short depletion timescales and large mass-loading factors in a burst-driven regime, with abundance ratios isolating star-formation efficiency, outflows, and inflows.