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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3 Pith papers cite this work. Polarity classification is still indexing.
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Fermi-LAT variability analysis of jetted AGNs finds mean damping timescales of ~100 days, greater amplitudes in FSRQs than BL Lacs, emission regions at 2-4.5 R_DT but 123-295 R_BLR, and correlations with luminosities, loudness, black hole mass, and Eddington ratio.
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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Characterizing the origins of gamma-ray variability of the jetted active galactic nuclei observed with the Fermi-LAT
Fermi-LAT variability analysis of jetted AGNs finds mean damping timescales of ~100 days, greater amplitudes in FSRQs than BL Lacs, emission regions at 2-4.5 R_DT but 123-295 R_BLR, and correlations with luminosities, loudness, black hole mass, and Eddington ratio.
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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.