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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Cross-correlation of CLAMATO Lyman-alpha forest with COSMOS galaxies yields stellar-mass-dependent biases of approximately 2.1, 3.2, and 3.8, corresponding to halo masses of log M_h ~ 10.5, 11.7, and 12.1 from Bolshoi-Planck mocks, with hints of enhanced low-mass star formation.
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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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Cross-correlations between the CLAMATO Lyman-alpha forest and galaxies within the COSMOS field
Cross-correlation of CLAMATO Lyman-alpha forest with COSMOS galaxies yields stellar-mass-dependent biases of approximately 2.1, 3.2, and 3.8, corresponding to halo masses of log M_h ~ 10.5, 11.7, and 12.1 from Bolshoi-Planck mocks, with hints of enhanced low-mass star formation.