juliensimon/cosmic-void-catalog

---
license: cc-by-4.0
pretty_name: "Cosmic Void Catalog"
language:
- en
description: "Catalog of 1,228 cosmic voids from SDSS, with positions, redshifts, radii, and density contrasts. Cosmic voids are vast underdense regions in the large-scale structure of the universe."
task_categories:
- tabular-classification
tags:
- space
- cosmic-void
- large-scale-structure
- cosmology
- sdss
- astronomy
- dark-energy
- open-data
- tabular-data
- parquet
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/cosmic_voids.parquet
default: true
---

# Cosmic Void Catalog

*Part of the [Astronomy Datasets](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743) and [Galaxies & Cosmology](https://huggingface.co/collections/juliensimon/galaxies-cosmology-datasets-6849da7f5e6455f6a7b2afb9) collections on Hugging Face.*

![Updated](https://img.shields.io/badge/dynamic/json?url=https://raw.githubusercontent.com/juliensimon/space-datasets/main/status.json&query=$["cosmic-voids"]&label=updated&color=brightgreen)

Catalog of **1,228** cosmic voids identified in the Sloan Digital Sky Survey (SDSS),
sourced from VizieR CDS Strasbourg.

## Dataset description

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. Voids are among the cleanest cosmological laboratories available because their interiors are dominated by dark energy rather than by the complex nonlinear gravitational dynamics that govern overdense regions.

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. The integrated Sachs-Wolfe (ISW) effect -- the late-time blueshift of CMB photons traversing growing voids -- provides independent evidence for dark energy.

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, testing the hypothesis that void galaxies evolve differently from their counterparts in denser regions.

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `sample` | string | Sample |
| `id` | int | Id |
| `ra_deg` | float | Right ascension J2000 (degrees) |
| `dec_deg` | float | Declination J2000 (degrees) |
| `redshift` | float | Void center redshift |
| `ngal` | int | Ngal |
| `v` | float | V |
| `radius_eff_mpc` | float | Effective void radius (Mpc) |
| `nmin` | float | Nmin |
| `delmin` | float | Delmin |
| `r` | float | R |
| `prob` | float | Prob |
| `dbound` | float | Dbound |

## 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

```python
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")

# Redshift distribution
if "redshift" in df.columns:
df["redshift"].dropna().hist(bins=30, edgecolor="black")
plt.xlabel("Redshift")
plt.ylabel("Count")
plt.title("Void Redshift Distribution")

# Sky distribution
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")
```

## Data source

Cosmic void catalog from the Sloan Digital Sky Survey (SDSS),
accessed via [VizieR](https://vizier.cds.unistra.fr/), CDS Strasbourg.

Primary source: Pan D.C., Vogeley M.S., Hoyle F., Choi Y.-Y., Park C., 2012, MNRAS, 421, 926.

## Update schedule

Semi-annual (January and July 1st at 07:30 UTC) via [GitHub Actions](https://github.com/juliensimon/space-datasets).

## Related datasets

- [desi-dr1-redshifts](https://huggingface.co/datasets/juliensimon/desi-dr1-redshifts) -- DESI DR1 galaxy redshifts
- [galaxy-clusters](https://huggingface.co/datasets/juliensimon/galaxy-clusters) -- Planck SZ galaxy clusters
- [pantheon-plus-sne-ia](https://huggingface.co/datasets/juliensimon/pantheon-plus-sne-ia) -- Pantheon+ Type Ia supernovae
- [planck-sz2-clusters](https://huggingface.co/datasets/juliensimon/planck-sz2-clusters) -- Planck SZ2 cluster catalog

## Pipeline

Source code: [juliensimon/space-datasets](https://github.com/juliensimon/space-datasets)

## Support

If you find this dataset useful, please give it a ❤️ on the [dataset page](https://huggingface.co/datasets/juliensimon/cosmic-void-catalog) and share feedback in the Community tab! Also consider giving a ⭐️ to the [space-datasets](https://github.com/juliensimon/space-datasets) repo.

## Citation

```bibtex
@dataset{cosmic_void_catalog,
author = {Simon, Julien},
title = {Cosmic Void Catalog},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/cosmic-void-catalog},
note = {Based on SDSS void catalogs via VizieR CDS Strasbourg}
}
```

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

许可协议：CC BY 4.0
数据集名称：宇宙空洞目录（Cosmic Void Catalog）
语言：英语
数据集描述：收录来自斯隆数字巡天（SDSS，Sloan Digital Sky Survey）的1228个宇宙空洞的目录，包含位置、红移、半径与密度对比度信息。宇宙空洞是宇宙大尺度结构中的广袤低密度区域。
任务类别：表格分类（tabular-classification）
标签：空间、宇宙空洞（cosmic-void）、大尺度结构（large-scale-structure）、宇宙学（cosmology）、斯隆数字巡天（sdss）、天文学、暗能量（dark-energy）、开放数据（open-data）、表格数据（tabular-data）、Parquet格式（parquet）
样本量范围：1000 < n < 10000
配置项：
- 配置名称：默认配置
数据文件：
- 训练拆分：data/cosmic_voids.parquet
为默认配置

# 宇宙空洞目录
*本数据集隶属于Hugging Face平台的[天文学数据集合集](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743)与[星系与宇宙学数据集合集](https://huggingface.co/collections/juliensimon/galaxies-cosmology-datasets-6849da7f5e6455f6a7b2afb9)。*

![Updated](https://img.shields.io/badge/dynamic/json?url=https://raw.githubusercontent.com/juliensimon/space-datasets/main/status.json&query=$["cosmic-voids"]&label=updated&color=brightgreen)

本目录收录了在斯隆数字巡天（SDSS）中识别出的**1228个**宇宙空洞，数据源自斯特拉斯堡天文数据中心（CDS，Centre de Données astronomiques de Strasbourg）的VizieR数据库。

## 数据集说明
宇宙空洞是宇宙大尺度结构中的广袤低密度区域，典型半径介于20至50百万秒差距（Mpc）。它们占据了宇宙的绝大部分体积，由构成宇宙网的纤维状结构、片状结构与星系团所环绕。宇宙空洞是目前最纯净的宇宙学实验平台之一，因为其内部以暗能量为主导，而非支配高密度区域的复杂非线性引力动力学过程。

宇宙空洞的属性是探究基础物理的有力探针。空洞尺寸函数（即空洞丰度随半径的变化关系）对物质密度参数、σ₈以及暗能量状态方程均十分敏感。基于堆叠空洞形状的阿尔科克-帕钦斯基（Alcock-Paczynski）检验可约束宇宙的膨胀历史。空洞的引力透镜轮廓可用于测量低密度区域的物质含量，并检验修正引力理论——因为空洞会放大广义相对论与f(R)引力等替代理论之间的差异。积分萨克斯-沃尔夫（ISW，Integrated Sachs-Wolfe）效应——即穿越正在演化的空洞的宇宙微波背景（CMB，Cosmic Microwave Background）光子的晚期蓝移现象——为暗能量的存在提供了独立观测证据。

本目录可用于开展空洞人口统计学、空间分布以及与其他大尺度结构示踪剂的相关性研究。通过与星系巡天数据进行交叉匹配，能够揭示星系属性（颜色、形态、恒星形成率）如何随大尺度环境变化，从而检验“空洞中的星系演化模式与高密度区域的星系存在显著差异”这一假说。

## 数据集字段结构
| 字段名 | 数据类型 | 说明 |
|--------|----------|------|
| `sample` | 字符串 | 样本分组 |
| `id` | 整数 | 空洞编号 |
| `ra_deg` | 浮点数 | J2000坐标系下的赤经（单位：度） |
| `dec_deg` | 浮点数 | J2000坐标系下的赤纬（单位：度） |
| `redshift` | 浮点数 | 空洞中心红移 |
| `ngal` | 整数 | 星系数量 |
| `v` | 浮点数 | 空洞体积 |
| `radius_eff_mpc` | 浮点数 | 有效空洞半径（单位：百万秒差距，Mpc） |
| `nmin` | 浮点数 | 最小过密因子 |
| `delmin` | 浮点数 | 最小密度对比度 |
| `r` | 浮点数 | 径向尺度参数 |
| `prob` | 浮点数 | 空洞识别置信度 |
| `dbound` | 浮点数 | 空洞边界密度对比度 |

## 快速统计信息
- 共收录**1228个**宇宙空洞
- 有效半径中位数：**54.1 百万秒差距（Mpc）**
- 最大空洞半径：**452.7 百万秒差距（Mpc）**
- 红移中位数：**0.480**
- 红移范围：**0.214** 至 **0.672**

## 使用示例
python
from datasets import load_dataset

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

# 绘制空洞尺寸分布直方图
import matplotlib.pyplot as plt
if "radius_eff_mpc" in df.columns:
df["radius_eff_mpc"].dropna().hist(bins=30, edgecolor="black")
plt.xlabel("有效半径（百万秒差距）")
plt.ylabel("数量")
plt.title("宇宙空洞尺寸分布")

# 绘制红移分布直方图
if "redshift" in df.columns:
df["redshift"].dropna().hist(bins=30, edgecolor="black")
plt.xlabel("红移")
plt.ylabel("数量")
plt.title("空洞红移分布")

# 绘制天球分布散点图
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="红移")
plt.xlabel("赤经（度）")
plt.ylabel("赤纬（度）")
plt.title("宇宙空洞天球分布")

## 数据来源
本宇宙空洞目录源自斯隆数字巡天（SDSS），数据通过斯特拉斯堡天文数据中心（CDS）的[VizieR数据库](https://vizier.cds.unistra.fr/)获取。
原始文献：Pan D.C.、Vogeley M.S.、Hoyle F.、Choi Y.-Y.、Park C.，2012年，《皇家天文学会月报》（MNRAS，Monthly Notices of the Royal Astronomical Society），第421卷，第926页。

## 更新计划
每半年更新一次（每年1月和7月1日07:30 UTC），更新流程通过[GitHub Actions](https://github.com/juliensimon/space-datasets)实现。

## 相关数据集
- [desi-dr1-redshifts](https://huggingface.co/datasets/juliensimon/desi-dr1-redshifts) —— DESI DR1星系红移数据集
- [galaxy-clusters](https://huggingface.co/datasets/juliensimon/galaxy-clusters) —— 普朗克SZ效应星系团数据集
- [pantheon-plus-sne-ia](https://huggingface.co/datasets/juliensimon/pantheon-plus-sne-ia) —— Pantheon+ Ia型超新星数据集
- [planck-sz2-clusters](https://huggingface.co/datasets/juliensimon/planck-sz2-clusters) —— 普朗克SZ2星系团目录

## 数据处理流程
源代码仓库：[juliensimon/space-datasets](https://github.com/juliensimon/space-datasets)

## 支持与反馈
如果您认为本数据集对您的研究有所帮助，请前往[数据集页面](https://huggingface.co/datasets/juliensimon/cosmic-void-catalog)为其点赞，并在社区标签页中分享您的反馈！同时也欢迎为[space-datasets](https://github.com/juliensimon/space-datasets)代码仓库点亮⭐️。

## 引用格式
bibtex
@dataset{cosmic_void_catalog,
author = {Simon, Julien},
title = {Cosmic Void Catalog},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/cosmic-void-catalog},
note = {Based on SDSS void catalogs via VizieR CDS Strasbourg}
}

## 许可协议
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)