A new deep-to-wide transfer function reduces mean redshift biases in Euclid tomographic bins by matching reference sample color distributions to the wide survey.
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2026 2representative citing papers
LSTM-MDNz improves photometric redshift point estimates and calibrated PDFs over a BNN baseline on HSC GalaxiesML, cutting outliers ~20% and enabling high-purity catalogs via z_conf.
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Euclid: Improving redshift distribution reconstruction using a deep-to-wide transfer function
A new deep-to-wide transfer function reduces mean redshift biases in Euclid tomographic bins by matching reference sample color distributions to the wide survey.
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Enhancing Photometric Redshift Estimation for LSST with a Hybrid LSTM-Mixture Density Network
LSTM-MDNz improves photometric redshift point estimates and calibrated PDFs over a BNN baseline on HSC GalaxiesML, cutting outliers ~20% and enabling high-purity catalogs via z_conf.