juliensimon/cosmic-void-catalog

Cosmic Void Catalog

Credit: NASA/ESA/STScI

Part of a dataset collection on Hugging Face.

Dataset description

Catalog of cosmic voids identified in the Sloan Digital Sky Survey (SDSS). Cosmic voids are vast underdense regions in the large-scale structure of the universe, typically 20-50 Mpc in radius. They occupy the majority of the volume of the universe and are bounded by filaments, walls, and clusters that form the cosmic web.

Void properties are powerful probes of fundamental physics. The void size function (abundance as a function of radius) is sensitive to the matter density parameter, sigma_8, and the dark energy equation of state. The Alcock-Paczynski test applied to stacked void shapes constrains the expansion history of the universe. Void lensing profiles measure the matter content of underdense regions and test modified gravity theories, since voids amplify the differences between general relativity and alternative theories such as f(R) gravity.

This catalog enables studies of void demographics, spatial distribution, and correlations with other large-scale structure tracers. Cross-matching with galaxy surveys reveals how galaxy properties (color, morphology, star formation rate) depend on large-scale environment.

This dataset is suitable for tabular classification tasks.

Schema

Column	Type	Description	Sample	Null %
ra_deg	float64	ICRS J2000.0 right ascension of the void center in degrees (0-360)	184.439	0.0%
dec_deg	float64	ICRS J2000.0 declination of the void center in degrees (-90 to +90)	37.133	0.0%
redshift	float64	Redshift of the void center; survey range typically 0.02 < z < 0.5	0.292	0.0%
radius_eff_mpc	float64	Effective (spherically-equivalent) void radius in Mpc; typical range 10-100 Mpc	452.732	0.0%

Quick stats

• 1,228 cosmic voids
• Median effective radius: 54.1 Mpc
• Largest void radius: 452.7 Mpc
• Median redshift: 0.480
• Redshift range: 0.214 to 0.672

Usage

from datasets import load_dataset

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

from datasets import load_dataset

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

# Void size distribution
import matplotlib.pyplot as plt
if "radius_eff_mpc" in df.columns:
    df["radius_eff_mpc"].dropna().hist(bins=30, edgecolor="black")
    plt.xlabel("Effective Radius (Mpc)")
    plt.ylabel("Count")
    plt.title("Cosmic Void Size Distribution")
    plt.show()

# Sky distribution sized by radius
plt.figure(figsize=(12, 6))
plt.scatter(df["ra_deg"], df["dec_deg"], s=df.get("radius_eff_mpc", 5)**2 / 50,
            alpha=0.5, c=df.get("redshift"), cmap="viridis")
plt.colorbar(label="Redshift")
plt.xlabel("RA (deg)")
plt.ylabel("Dec (deg)")
plt.title("Cosmic Void Sky Distribution")
plt.show()

Data source

https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/MNRAS/421/926

Related datasets

• juliensimon/desi-dr1-redshifts

• juliensimon/galaxy-clusters

• juliensimon/pantheon-plus-sne-ia

• juliensimon/planck-sz2-clusters

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{cosmic_void_catalog,
  title = {Cosmic Void Catalog},
  author = {juliensimon},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/cosmic-void-catalog},
  publisher = {Hugging Face}
}

License

CC-BY-4.0