No CMB-consistent CPL dark energy model can simultaneously fit both the BAOtr and DESI datasets; the 3.7-sigma disagreement at z=0.51 sets an irreducible floor.
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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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On the origin of the BAOtr-DESI tension
No CMB-consistent CPL dark energy model can simultaneously fit both the BAOtr and DESI datasets; the 3.7-sigma disagreement at z=0.51 sets an irreducible floor.
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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.