Redshift-bin-optimized color cuts using unWISE photometry reduce stellar contamination in the DES Y3 MagLim lens sample by 1.3-5.5% varying across bins and footprint.
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AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
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Taming Additive Systematics via Redshift-Bin-Optimized Star-Galaxy Separation
Redshift-bin-optimized color cuts using unWISE photometry reduce stellar contamination in the DES Y3 MagLim lens sample by 1.3-5.5% varying across bins and footprint.
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Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.