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juliensimon
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veron-agn-quasar-catalog

Tasks:
Tabular Classification
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
space
astronomy
agn
quasars
active-galactic-nuclei
seyfert
Libraries:
Datasets
pandas
Polars
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metadata 
license: other
license_name: vizier-scientific-use
license_link: https://cds.unistra.fr/vizier-org/licences_vizier.html
pretty_name: Veron-Cetty AGN/Quasar Catalog (13th edition)
language:
  - en
description: >-
  The Veron-Cetty & Veron Quasars and Active Nuclei catalog, 13th edition
  (VizieR VII/258) — the canonical reference compilation of 168,940 quasars and
  active galactic nuclei published through 2010, cov
task_categories:
  - tabular-classification
tags:
  - space
  - astronomy
  - agn
  - quasars
  - active-galactic-nuclei
  - seyfert
  - blazars
  - bl-lac
  - vizier
  - open-data
  - tabular-data
  - parquet
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/veron_agn_catalog.parquet
    default: true

Veron-Cetty AGN/Quasar Catalog (13th edition)

Artist concept of a supermassive black hole with a relativistic jet — AGN engine

Credit: NASA/JPL-Caltech

Part of a dataset collection on Hugging Face.

Dataset description

The Veron-Cetty & Veron Quasars and Active Nuclei catalog, 13th edition (VizieR VII/258) — the canonical reference compilation of 168,940 quasars and active galactic nuclei published through 2010, covering optical/UV-selected quasars, X-ray-selected AGN, radio galaxies, and BL Lac objects.

The catalog merges every published QSO and AGN identification with a homogeneous position, spectroscopic redshift, optical photometry, B-V and U-B colors, absolute magnitude, and (where available) radio fluxes at 6 cm and 20 cm. It remains the standard look-up source for cross-matching unidentified sources against the established AGN population, for selecting training/test samples by spectral subclass (Seyfert 1, Seyfert 2, LINER, BL Lac), and for population statistics in redshift, luminosity, and color spaces.

Object class column conventions: 'Q' marks quasars (M_V <= -23, brighter than the historical QSO/AGN boundary), 'A' marks lower-luminosity AGN (M_V > -23), and 'B' marks BL Lac objects. The spectral classification column captures Seyfert sub-type and special spectral phenomena. Use this dataset alongside juliensimon/milliquas (the more recent Million Quasars Catalog that supersedes Veron for survey-era discoveries), juliensimon/gaia-dr3-qso-candidates (Gaia-selected QSOs), juliensimon/black-hole-catalog, juliensimon/4xmm-dr14-xray-sources, and juliensimon/icrf3-reference-frame for AGN cross-mission analysis.

This dataset is suitable for tabular classification tasks.

Schema

Column Type Description Sample Null %
object_class str Object classification: 'Q' = quasar (M_abs <= -23, brighter than -23), 'A' = AGN (M_abs > -23, fainter), 'B' = BL Lac object Q 0.0%
radio_flag str Radio-detection flag: '*' = detected at radio frequencies, blank = not detected (or no radio coverage) * 11.3%
name str Primary designation (catalog and survey ID), typically 'SDSS Jhhmm+ddmm', '4C+nn.nn', 'PG hhmm+ddd', etc. SDSS J03003+0009 0.0%
position_origin str Origin code for the J2000 coordinates: 'O' = optical position, 'R' = radio position, 'X' = X-ray, etc. O 2.5%
ra float64 Right ascension (degrees, J2000) 45.07541666666666 0.0%
dec float64 Declination (degrees, J2000) 0.1524999999999999 0.0%
flux_20cm_mjy float64 Radio flux density at 20 cm (1.4 GHz) in mJy where detected 0.0 89.2%
flux_20cm_reference float64 Bibliographic reference code for the 20 cm flux measurement 2068.0 64.6%
redshift float64 Spectroscopic or photometric redshift z (dimensionless); range ~0 to ~7 in this compilation 2.23 0.4%
spectral_classification str Spectral subclass code: 'S1' = Seyfert 1, 'S2' = Seyfert 2, 'S1.5'/'S1.9' = intermediate types, 'NLAGN' = narrow-line AGN, 'HP' = high polarization, 'LINER' = LINER, blank = standard QSO S2 82.7%
magnitude_band str Photometric system letter for the magnitude: 'V' = Johnson V, 'B' = blue, 'R' = red, 'g' = SDSS g, etc. * 81.4%
apparent_magnitude float64 Apparent magnitude in the band identified by magnitude_band; brighter sources have smaller values 20.45 0.4%
color_bv float64 Johnson B-V color index (mag); positive values indicate redder colors 0.28 22.1%
color_ub float64 Johnson U-B color index (mag); the 'UV excess' (negative U-B) is the historical AGN selection signature -0.18 9.7%
absolute_magnitude float64 Absolute V magnitude M_V (mag); the Q/A class boundary is M_V = -23. Computed assuming H_0 = 71, Omega_M = 0.27, Omega_lambda = 0.73 -24.4 0.7%
finding_chart_reference float64 Bibliographic reference code for a published finding chart of the source 529.0 93.5%
magnitude_reference float64 Bibliographic reference code for the magnitude measurement 2068.0 9.1%
redshift_reference float64 Bibliographic reference code for the redshift 2068.0 0.0%

Quick stats

  • 168,940 quasars + AGN + BL Lac objects in the canonical compilation
  • 133,335 quasars (M_V <= -23) + 34,231 lower-luminosity AGN + 1,374 BL Lac objects
  • 149,874 sources with detected radio counterparts (6cm or 20cm)
  • Redshift range: 0.00 to 6.43 (median 1.35)
  • Absolute magnitude (M_V): -31.5 (most luminous) to -7.8 (faintest)

Usage 

from datasets import load_dataset

ds = load_dataset("juliensimon/veron-agn-quasar-catalog", split="train")
df = ds.to_pandas()

from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np

df = load_dataset("juliensimon/veron-agn-quasar-catalog", split="train").to_pandas()

# Hubble diagram: absolute magnitude vs redshift, colored by class
mask = df["redshift"].notna() & df["absolute_magnitude"].notna()
fig, ax = plt.subplots(figsize=(10, 6))
for cls, color in zip(["Q", "A", "B"], ["blue", "orange", "red"]):
    sub = df[mask & (df["object_class"] == cls)]
    ax.scatter(sub["redshift"], sub["absolute_magnitude"], s=2, alpha=0.3, color=color, label=cls)
ax.axhline(-23, color="grey", linestyle="--", label="QSO/AGN boundary")
ax.invert_yaxis()  # brighter = up
ax.set_xscale("log")
ax.set_xlabel("Redshift z")
ax.set_ylabel("Absolute V magnitude")
ax.legend()
plt.tight_layout()
plt.show()

# Seyfert sub-type breakdown
print(df["spectral_classification"].value_counts().head(15))

Data source

https://cdsarc.cds.unistra.fr/viz-bin/cat/VII/258

Related datasets

If you find this dataset useful, please consider giving it a like on Hugging Face. It helps others discover it.

About the author

Created by Julien Simon — AI Operating Partner at Fortino Capital. Part of the Space Datasets collection.

Citation 

@dataset{veron_agn_quasar_catalog,
  title = {Veron-Cetty AGN/Quasar Catalog (13th edition)},
  author = {juliensimon},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/veron-agn-quasar-catalog},
  publisher = {Hugging Face}
}

License

VizieR Scientific-Use Terms