Datasets:

juliensimon
/

quasar-catalog

license: cc-by-4.0
pretty_name: Quasar & AGN Catalog
language:
  - en
description: >-
  Catalog of quasars and active galactic nuclei from SIMBAD -- quasars, Seyfert
  galaxies, blazars, and LINERs with positions and classifications.  Active
  galactic nuclei are galaxies whose central super
task_categories:
  - tabular-classification
tags:
  - space
  - quasar
  - agn
  - blazar
  - seyfert
  - cosmology
  - astronomy
  - simbad
  - open-data
  - tabular-data
  - parquet
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/quasars.parquet
    default: true

Quasar & AGN Catalog

Deep field image revealing distant galaxies and quasars

Credit: NASA/ESA/STScI

Part of a dataset collection on Hugging Face.

Dataset description

Catalog of quasars and active galactic nuclei from SIMBAD -- quasars, Seyfert galaxies, blazars, and LINERs with positions and classifications.

Active galactic nuclei are galaxies whose central supermassive black holes are actively accreting matter, releasing enormous amounts of energy across the electromagnetic spectrum. Quasars, the most luminous subclass, can outshine their entire host galaxy by factors of a hundred or more and are visible at cosmological distances, making them powerful probes of the early universe. The different AGN categories in this catalog -- Seyfert 1 and 2 galaxies, blazars, BL Lac objects, and LINERs -- are thought to represent different viewing angles and accretion rates of the same underlying phenomenon, unified under orientation-dependent models.

These objects are critical for multiple areas of astrophysics. Quasars serve as background beacons for studying the intergalactic medium through absorption-line spectroscopy, they anchor the International Celestial Reference Frame (ICRF) used for precision astrometry, and their redshift distribution traces the growth history of supermassive black holes across cosmic time. Blazars, whose relativistic jets point nearly along our line of sight, are among the brightest persistent sources in the gamma-ray sky and are candidate sources of high-energy cosmic neutrinos.

The SIMBAD database aggregates classifications from thousands of publications, providing a heterogeneous but broadly representative census of known AGN. This catalog is useful for cross-matching with multi-wavelength surveys, selecting targets for spectroscopic follow-up, and building training sets for machine-learning classification of AGN from photometric data.

This dataset is suitable for tabular classification tasks.

Schema

Column	Type	Description	Sample	Null %
`name`	str	Primary SIMBAD identifier (e.g. 'QSO J1230+1223' or '3C 273'); unique within SIMBAD but may differ from other catalog designations	10C J093827+302802	0.0%
`ra_deg`	float64	Right ascension of the AGN nucleus in the ICRS J2000.0 frame, decimal degrees (0-360)	144.61387810042916	0.0%
`dec_deg`	float64	Declination of the AGN nucleus in the ICRS J2000.0 frame, decimal degrees (-90 to +90)	30.467134118115275	0.0%
`object_type`	str	SIMBAD machine-readable type code: 'QSO' = radio-quiet quasar, 'AGN' = broad-line active galactic nucleus, 'Sy1' = Seyfert 1 (broad + narrow lines, type-1 viewing angle), 'Sy2' = Seyfert 2 (narrow lines only, obscured nucleus), 'BLL' = BL Lac object (featureless continuum, jet pointing toward observer), 'Bla' = blazar (BL Lac or FSRQ with relativistic jet), 'LIN' = LINER (Low Ionization Nuclear Emission Region, weak AGN activity)	AGN	0.0%
`agn_category`	str	Human-readable category derived from object_type: one of 'Quasar', 'AGN', 'Seyfert 1', 'Seyfert 2', 'BL Lac Object', 'Blazar', 'LINER'; useful for grouped analysis without parsing SIMBAD codes	Active Galactic Nucleus	0.0%

Quick stats

50,000 objects total
11,443 quasars (QSO)
19,446 Seyfert galaxies (Sy1 + Sy2)
1,959 blazars / BL Lac objects
8,643 general AGN
8,509 LINERs

Usage

from datasets import load_dataset

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

from datasets import load_dataset

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

# AGN type breakdown
print(df["agn_category"].value_counts())

# Sky distribution by type
import matplotlib.pyplot as plt
for cat in ["Quasar", "Seyfert 1", "BL Lac Object"]:
    sub = df[df["agn_category"] == cat]
    plt.scatter(sub["ra_deg"], sub["dec_deg"], s=0.5, alpha=0.3, label=cat)
plt.xlabel("RA (deg)")
plt.ylabel("Dec (deg)")
plt.legend(markerscale=10)
plt.title("AGN Sky Distribution by Type")
plt.show()

Data source

https://simbad.u-strasbg.fr/simbad/

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{quasar_catalog,
  title = {Quasar & AGN Catalog},
  author = {Simon, Julien},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/quasar-catalog},
  note = {Derived from SIMBAD, Centre de Données astronomiques de Strasbourg (CDS), https://simbad.u-strasbg.fr/simbad/},
  publisher = {Hugging Face}
}

License

CC-BY-4.0