juliensimon/wolf-rayet-stars

---
license: cc-by-4.0
pretty_name: "Galactic Wolf-Rayet Stars"
language:
- en
description: "Catalog of 380 Galactic Wolf-Rayet stars with Gaia DR2 astrometry, distances, spectral types, and photometry. Based on Rate & Crowther (2020, MNRAS 493, 1512), sourced via VizieR CDS Strasbourg."
task_categories:
- tabular-classification
tags:
- space
- stars
- wolf-rayet
- massive-stars
- astronomy
- open-data
- tabular-data
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/wolf_rayet_stars.parquet
default: true
---

# Galactic Wolf-Rayet Stars

*Part of the [Astronomy Datasets](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743) collection on Hugging Face.*

Catalog of **380** Galactic Wolf-Rayet stars — massive evolved stars with powerful stellar
winds and broad emission lines. Wolf-Rayet stars represent a brief but spectacular late stage in
the lives of the most massive stars (>25 solar masses), just before they explode as supernovae.

## Dataset description

Wolf-Rayet (WR) stars are among the hottest and most luminous stars known, with surface
temperatures of 30,000-200,000 K and luminosities up to a million times the Sun. Their spectra
are dominated by broad emission lines from helium, nitrogen (WN subtype), carbon (WC subtype),
or oxygen (WO subtype), produced by their extreme stellar winds losing mass at rates of
10^-5 solar masses per year.

This catalog is based on Rate & Crowther (2020), which combined the most complete census of
Galactic WR stars with Gaia DR2 parallaxes to derive distances, luminosities, and spatial
distribution. The dataset includes astrometric positions, spectral classifications, Gaia and
infrared photometry, and distance estimates.

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `wr_number` | string | WR star designation (e.g. "WR1", "WR136") |
| `wr_flag` | string | Flag on WR name |
| `spectral_type` | string | Spectral type (e.g. "WN4b", "WC7+O5-8", "WO2") |
| `wr_subtype` | string | WR subtype: WN (nitrogen), WC (carbon), WO (oxygen) |
| `is_binary` | bool | Whether spectral type indicates a binary system |
| `name` | string | Alternative name (e.g. HD number) |
| `ra_deg` | float64 | Right ascension ICRS at Ep=2015.5 (degrees) |
| `dec_deg` | float64 | Declination ICRS at Ep=2015.5 (degrees) |
| `parallax_mas` | float64 | Zero-point corrected Gaia DR2 parallax (mas) |
| `parallax_error_mas` | float64 | Error on parallax (mas) |
| `distance_kpc` | float64 | Distance from the Sun (kpc) |
| `distance_upper_error_kpc` | float64 | Upper error on distance (kpc) |
| `distance_lower_error_kpc` | float64 | Lower error on distance (kpc) |
| `galactic_height_pc` | float64 | Distance from Galactic mid-plane (pc) |
| `galactic_height_upper_error_pc` | float64 | Upper error on Galactic height (pc) |
| `galactic_height_lower_error_pc` | float64 | Lower error on Galactic height (pc) |
| `gaia_g_mag` | float64 | Gaia DR2 G-band magnitude |
| `gaia_bp_rp` | float64 | Gaia DR2 BP-RP colour index |
| `astrometric_excess_noise` | float64 | Gaia DR2 astrometric excess noise |
| `log_luminosity` | float64 | Log stellar luminosity (solar units) |
| `error_flag` | string | Error flag |
| `ks_mag` | float64 | 2MASS Ks-band apparent magnitude |
| `j_ks_color` | float64 | J-Ks colour index |
| `h_ks_color` | float64 | H-Ks colour index |
| `ks_extinction` | float64 | Ks-band extinction (mag) |
| `ks_abs_mag` | float64 | Absolute Ks-band magnitude of WR star |

## Quick stats

- **380** Galactic Wolf-Rayet stars
- **220** WN (nitrogen sequence), **145** WC (carbon sequence), **4** WO (oxygen sequence)
- **64** spectroscopic binaries
- **380** with Gaia-based distance estimates (median 2.9 kpc)
- **96** with luminosity measurements

## Usage

```python
from datasets import load_dataset

ds = load_dataset("juliensimon/wolf-rayet-stars", split="train")
df = ds.to_pandas()

# WN vs WC distribution
print(df["wr_subtype"].value_counts())

# Nearest WR stars
nearest = df.nsmallest(10, "distance_kpc")[["wr_number", "spectral_type", "distance_kpc", "name"]]
print(nearest)

# Luminosity distribution by subtype
import matplotlib.pyplot as plt
for st in ["WN", "WC"]:
sub = df[df["wr_subtype"] == st].dropna(subset=["log_luminosity"])
plt.hist(sub["log_luminosity"], bins=15, alpha=0.6, label=st)
plt.xlabel("log(L/L_sun)")
plt.ylabel("Count")
plt.legend()
plt.title("Wolf-Rayet Luminosity Distribution")
```

## Data source

Rate G., Crowther P.A. (2020), "Unlocking Galactic Wolf-Rayet stars with Gaia DR2",
*Monthly Notices of the Royal Astronomical Society*, 493, 1512.
Accessed via [VizieR](https://vizier.cds.unistra.fr/) (J/MNRAS/493/1512), CDS Strasbourg.

## Related datasets

- [ob-stars](https://huggingface.co/datasets/juliensimon/ob-stars) -- OB stellar catalog
- [gcvs-variable-stars](https://huggingface.co/datasets/juliensimon/gcvs-variable-stars) -- General Catalogue of Variable Stars
- [pulsar-catalog](https://huggingface.co/datasets/juliensimon/pulsar-catalog) -- ATNF Pulsar Catalogue

## Pipeline

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

## Citation

```bibtex
@dataset{wolf_rayet_stars,
author = {Simon, Julien},
title = {Galactic Wolf-Rayet Stars},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/wolf-rayet-stars},
note = {Based on Rate & Crowther (2020, MNRAS 493, 1512) via VizieR CDS Strasbourg}
}
```

## License

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