Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.
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A coordinated Rubin-DESI supernova survey could distinguish dynamical dark energy from Lambda CDM at over 5 sigma in one year using 2300 spectroscopically confirmed SNe Ia at low redshift.
IRMaGiC extends redMaGiC to z=1-2 using joint LSST optical and Roman infrared data, reducing photo-z scatter and bias for LRGs.
citing papers explorer
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Photometric Redshift PDFs via Neural Network Classification for DESI Legacy Imaging Surveys and Pan-STARRS
Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.
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Testing $\Lambda$CDM versus dynamical dark energy in one year: A DESI spectroscopic follow-up program for Rubin supernovae
A coordinated Rubin-DESI supernova survey could distinguish dynamical dark energy from Lambda CDM at over 5 sigma in one year using 2300 spectroscopically confirmed SNe Ia at low redshift.
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IRMaGiC: Extending Luminous Red Galaxy Selection into the Infrared with Joint Rubin Observatory's Large Survey of Space Time and Roman's High Latitude Imaging Survey
IRMaGiC extends redMaGiC to z=1-2 using joint LSST optical and Roman infrared data, reducing photo-z scatter and bias for LRGs.