juliensimon/planetary-nebulae

---
license: cc-by-4.0
pretty_name: "Planetary Nebulae (MUSE Survey)"
language:
- en
description: "Planetary nebulae identified by MUSE (Multi Unit Spectroscopic Explorer). Sourced via VizieR CDS Strasbourg."
task_categories:
- tabular-classification
tags:
- space
- planetary-nebula
- muse
- astronomy
- open-data
size_categories:
- 1K<n<10K
---

# Planetary Nebulae (MUSE Survey)

Catalog of **1,715** planetary nebulae from the MUSE (Multi Unit Spectroscopic
Explorer) survey, with positions, velocities, and morphological classifications.

## Dataset description

Planetary nebulae (PNe) are glowing shells of ionized gas expelled by intermediate-mass
stars at the end of their lives. They are important distance indicators and tracers of
stellar populations and chemical enrichment. This catalog from Jacoby et al. (2024)
presents PNe identified and characterized using the MUSE integral-field spectrograph
on ESO's Very Large Telescope, providing unprecedented spectroscopic detail.

## Quick stats

- **1,715** planetary nebulae
- **0** with radial velocity measurements
- **0** with morphological classifications

## Usage

```python
from datasets import load_dataset

ds = load_dataset("juliensimon/planetary-nebulae", split="train")
df = ds.to_pandas()

# PNe with velocity measurements
if "velocity_kms" in df.columns:
with_vel = df.dropna(subset=["velocity_kms"])
print(f"{len(with_vel):,} PNe with velocities")
print(f"Velocity range: {with_vel['velocity_kms'].min():.0f} to {with_vel['velocity_kms'].max():.0f} km/s")

# Sky distribution
import matplotlib.pyplot as plt
plt.scatter(df["ra_deg"], df["dec_deg"], s=1, alpha=0.5)
plt.xlabel("RA (deg)")
plt.ylabel("Dec (deg)")
plt.title("MUSE Planetary Nebulae")
```

## Data source

Jacoby, G.H. et al. (2024), "Planetary Nebulae from the MUSE Survey",
ApJS, 271, 40. Accessed via [VizieR](https://vizier.cds.unistra.fr/), CDS Strasbourg.

## Pipeline

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

## License

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

license: CC-BY-4.0
pretty_name: "行星状星云（MUSE巡天，Planetary Nebulae (MUSE Survey)）"
language:
- en
description: "由多单元光谱探测器（Multi Unit Spectroscopic Explorer，简称MUSE）识别的行星状星云，数据源自斯特拉斯堡CDS的VizieR数据库。"
task_categories:
- tabular-classification
tags:
- space
- planetary-nebula
- muse
- astronomy
- open-data
size_categories:
- 1K<n<10K

# 行星状星云（MUSE巡天）

本数据集收录来自MUSE巡天的1715颗行星状星云，涵盖其位置、视向速度与形态分类信息。

## 数据集说明

行星状星云（Planetary Nebulae，简称PNe）是中等质量恒星在演化末期抛射出的电离气体发光壳层，它们是重要的距离指示器，同时可作为恒星族群与化学增丰过程的示踪物。本目录由Jacoby等人（2024）编制，收录了利用欧洲南方天文台（European Southern Observatory，简称ESO）甚大望远镜（Very Large Telescope，简称VLT）上的MUSE积分场光谱仪识别并表征的行星状星云，提供了前所未有的光谱细节。

## 快速统计数据

- **1715** 行星状星云
- **0** 颗拥有视向速度测量数据
- **0** 颗拥有形态分类数据

## 使用方法

python
from datasets import load_dataset

ds = load_dataset("juliensimon/planetary-nebulae", split="train")
df = ds.to_pandas()

# 筛选带有速度测量数据的行星状星云
if "velocity_kms" in df.columns:
with_vel = df.dropna(subset=["velocity_kms"])
print(f"{len(with_vel):,} 颗带有速度数据的行星状星云")
print(f"速度范围：{with_vel['velocity_kms'].min():.0f} 至 {with_vel['velocity_kms'].max():.0f} km/s")

# 天球分布
import matplotlib.pyplot as plt
plt.scatter(df["ra_deg"], df["dec_deg"], s=1, alpha=0.5)
plt.xlabel("RA (deg)")
plt.ylabel("Dec (deg)")
plt.title("MUSE Planetary Nebulae")

## 数据来源

Jacoby, G.H. 等人（2024），《MUSE巡天中的行星状星云》，《天体物理学杂志增刊》（ApJS），271卷，第40页。数据通过斯特拉斯堡CDS的[VizieR](https://vizier.cds.unistra.fr/)数据库获取。

## 数据处理流程

源代码：[juliensimon/space-datasets](https://github.com/juliensimon/space-datasets)

## 许可证

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