VAR-PZ uses DRW-modeled variability to create photo-z priors that reduce catastrophic outliers in AGN redshift estimates from 32% to below 7% when combined with SED fitting, validated on SDSS data and LSST simulations.
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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.
Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.
citing papers explorer
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VAR-PZ: Constraining the Photometric Redshifts of Quasars using Variability
VAR-PZ uses DRW-modeled variability to create photo-z priors that reduce catastrophic outliers in AGN redshift estimates from 32% to below 7% when combined with SED fitting, validated on SDSS data and LSST simulations.
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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.
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Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue
Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.