juliensimon/cns5-nearby-stars

---
license: cc-by-4.0
pretty_name: "Catalogue of Nearby Stars (CNS5)"
language:
- en
description: "The fifth edition of the Catalogue of Nearby Stars within 25 parsecs (Golovin+ 2023), with astrometry, photometry, and cross-identifiers. Sourced via VizieR CDS Strasbourg."
task_categories:
- tabular-classification
tags:
- space
- stars
- solar-neighborhood
- nearby-stars
- astronomy
- open-data
- tabular-data
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/cns5_nearby_stars.parquet
default: true
---

# Catalogue of Nearby Stars (CNS5)

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

The fifth edition of the Catalogue of Nearby Stars (CNS5) is a comprehensive census of **5,909**
stellar systems within 25 parsecs of the Sun. It provides astrometric, photometric, and
cross-identification data for the solar neighborhood, compiled from Gaia EDR3, Hipparcos, 2MASS,
and WISE.

## Dataset description

Understanding the stellar population of the solar neighborhood is fundamental to astrophysics.
The CNS5 (Golovin, Reffert, Just, Jordan, Vani & Jahreiss 2023, A&A 670, A19) extends the classic
Gliese & Jahreiss nearby-star catalogs using Gaia EDR3 parallaxes as the primary distance
indicator. It includes all known stars with trigonometric parallax placing them within 25 pc,
with multi-band photometry (Gaia G/BP/RP, 2MASS JHKs, WISE W1-W4), proper motions, and radial
velocities where available.

Each entry includes coordinates, parallax (and derived distance), proper motion, radial velocity,
Gaia and infrared magnitudes, and cross-identifiers (Gliese-Jahreiss, Hipparcos, Gaia DR3, SIMBAD).

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `cns5_id` | int | CNS5 designation number |
| `gj_name` | string | Gliese-Jahreiss identifier |
| `component` | string | Component suffix for binary/multiple systems |
| `n_components` | float64 | Number of components in the system |
| `problematic_flag` | float64 | Problematic entry flag |
| `gj_primary` | string | GJ number of the primary component |
| `gaia_dr3_id` | int64 | Gaia EDR3 source identifier |
| `hip_id` | float64 | Hipparcos identifier |
| `ra_deg` | float64 | Right ascension J2000 (degrees) |
| `dec_deg` | float64 | Declination J2000 (degrees) |
| `epoch` | float64 | Reference epoch for coordinates |
| `parallax_mas` | float64 | Trigonometric parallax (mas) |
| `parallax_error_mas` | float64 | Parallax uncertainty (mas) |
| `pm_ra_mas_yr` | float64 | Proper motion in RA (mas/yr) |
| `pm_dec_mas_yr` | float64 | Proper motion in Dec (mas/yr) |
| `radial_velocity_km_s` | float64 | Radial velocity (km/s) |
| `g_mag` | float64 | Gaia G magnitude |
| `bp_mag` | float64 | Gaia BP magnitude |
| `rp_mag` | float64 | Gaia RP magnitude |
| `j_mag` | float64 | 2MASS J magnitude |
| `h_mag` | float64 | 2MASS H magnitude |
| `ks_mag` | float64 | 2MASS Ks magnitude |
| `w1_mag` | float64 | WISE W1 magnitude |
| `w2_mag` | float64 | WISE W2 magnitude |
| `w3_mag` | float64 | WISE W3 magnitude |
| `w4_mag` | float64 | WISE W4 magnitude |
| `distance_pc` | float64 | Distance in parsecs (derived from parallax) |
| `simbad_name` | string | SIMBAD object name |

*Plus error columns and reference columns — 56 columns total.*

## Quick stats

- **5,909** stellar entries within 25 pc
- **5,237** with Gaia DR3 cross-match
- **1,586** with radial velocity
- **5,908** with SIMBAD identification
- Median distance: **19.9 pc**, nearest: **1.30 pc**

## Usage

```python
from datasets import load_dataset

ds = load_dataset("juliensimon/cns5-nearby-stars", split="train")
df = ds.to_pandas()

# Stars within 5 parsecs (the immediate solar neighborhood)
nearby = df[df["distance_pc"] <= 5].sort_values("distance_pc")
print(f"{len(nearby)} stars within 5 pc")
print(nearby[["simbad_name", "distance_pc", "g_mag"]].head(10))

# Distribution of stellar distances
import matplotlib.pyplot as plt
df["distance_pc"].dropna().hist(bins=50)
plt.xlabel("Distance (pc)")
plt.ylabel("Count")
plt.title("CNS5: Distribution of Nearby Star Distances")

# Color-magnitude diagram
valid = df.dropna(subset=["bp_mag", "rp_mag", "g_mag", "parallax_mas"])
valid["abs_g"] = valid["g_mag"] + 5 * (1 + valid["parallax_mas"].apply(lambda p: __import__('math').log10(p / 1000)))
valid["bp_rp"] = valid["bp_mag"] - valid["rp_mag"]
plt.scatter(valid["bp_rp"], valid["abs_g"], s=0.3, alpha=0.4)
plt.gca().invert_yaxis()
plt.xlabel("BP - RP (mag)")
plt.ylabel("Absolute G (mag)")
plt.title("CNS5 HR Diagram")
```

## Data source

[Catalogue of Nearby Stars (CNS5)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/A+A/670/A19)
(Golovin A., Reffert S., Just A., Jordan S., Vani A., Jahreiss H., 2023, A&A, 670, A19),
accessed via [VizieR](https://vizier.cds.unistra.fr/), CDS Strasbourg.

## Related datasets

- [hipparcos](https://huggingface.co/datasets/juliensimon/hipparcos) -- Hipparcos main catalog
- [brown-dwarfs](https://huggingface.co/datasets/juliensimon/brown-dwarfs) -- Brown dwarfs within 40 pc
- [open-clusters](https://huggingface.co/datasets/juliensimon/open-clusters) -- Open star clusters

## Pipeline

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

## Citation

```bibtex
@dataset{cns5_nearby_stars,
author = {Simon, Julien},
title = {Catalogue of Nearby Stars (CNS5)},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/cns5-nearby-stars},
note = {Based on CNS5 (Golovin et al. 2023, A&A 670, A19) via VizieR CDS Strasbourg}
}
```

## License

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