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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
astro-ph.GA 2representative citing papers
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
-
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.
-
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.