In addition to the primary SDSS photometry and spectroscopy, there are a few extra catalogs created by our collaborators that are distributed through the SAS. These Value-Added Catalogs (VACs) are listed below, and include catalogs that were released in earlier data releases.
Updated redshifts and classifications of Fermi-detected sources within SDSS-V
Multi-component optical spectroscopic classifications, redshifts, and jet fractions estimation for 707 Fermi blazar candidates identified in SDSS-V DR20
Catalog Data
Abstract
This Value-Added Catalogue (VAC) provides multi-component spectral fitting results for 707 confirmed Fermi blazar candidates identified by cross-matching the Fermi/4FGL-DR4 catalogue (Ballet et al. 2023) with the SDSS-V Data Release 20 spectroscopic database. A physically motivated pipeline decomposes each optical spectrum into a non-thermal power-law jet continuum combined with elliptical galaxy (Polletta et al. 2007) , synthetic QSO (Temple et al. 2021) or emission-line templates, enabling jet-aware classifications that are not provided by the SDSS automated pipeline. For each source the catalogue provides: (1) best-fit spectral model and blazar subclass (BL Lac or FSRQ candidate); (2) spectroscopic redshift with 1σ uncertainty; (3) reduced χ² for the multi-component model fit; (4) optical jet flux fraction (f_jet); (5) power-law slope parameters α and δ describing the jet continuum shape; and (6) observation MJD. Redshifts are validated against the Third Catalog of Hard Fermi-LAT Sources (Ajello et al. 2017), achieving a 10.4% reduction in the catastrophic outlier fraction relative to the SDSS automated pipeline. Independent validation using WISE infrared photometry (Wright et al. 2010) confirms that 96.6% of SDSS-misclassified stellar sources occupy the canonical blazar region of the infrared colour-colour diagram (Massaro et al. 2011) . The catalogue is accompanied by blazarkit, an open-source Python package for streaming, visualising, and analysing individual source spectra and multi-component fits (Nlowie et al., submitted). There is a Python notebook tutorial available for this VAC, which you can find on GitHub here.
Catalog last modified: 2026-07-30 12:12:10