Datasets:
license: other
license_name: cc-by-nc-3.0-igo
license_link: https://creativecommons.org/licenses/by-nc/3.0/igo/
pretty_name: Gaia DR3 Stellar Rotation Periods
language:
- en
description: >-
The Gaia DR3 rotation modulation catalog contains stellar rotation periods
derived from photometric variability detected by the ESA Gaia mission. Each
entry represents a star whose periodic brightness
task_categories:
- tabular-regression
tags:
- space
- gaia
- stellar-rotation
- variable-stars
- stellar-activity
- esa
- astronomy
- open-data
- tabular-data
- parquet
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/gaia_dr3_rotation_modulation.parquet
default: true
Gaia DR3 Stellar Rotation Periods
Credit: NASA/GSFC
Part of a dataset collection on Hugging Face.
Dataset description
The Gaia DR3 rotation modulation catalog contains stellar rotation periods derived from photometric variability detected by the ESA Gaia mission. Each entry represents a star whose periodic brightness variations — caused by dark starspots rotating with the star — were detected and modeled by Gaia's variability processing pipeline. The key output is best_rotation_period: the stellar rotation period in days.
Stellar rotation is one of the most powerful astrophysical diagnostics available. Stars spin down as they age through a process called magnetic braking, where stellar winds carry away angular momentum. This age-rotation relation (gyrochronology) allows stellar ages to be estimated from the rotation period and color alone, complementing classical isochrone fitting. Young, active stars rotate rapidly (periods of 1–10 days), while middle-aged stars like the Sun rotate slowly (~25 days), and old stars can have periods exceeding 50 days.
The max_activity_index_g column quantifies the amplitude of brightness modulation due to starspots. High activity indices indicate strong magnetic activity, which is directly correlated with X-ray and UV emission, flare rates, and thus the radiation environment experienced by any orbiting planets. Fast-rotating, active stars are therefore important targets for planetary habitability studies.
With approximately 82,000 rotation periods available through the Gaia archive TAP service, this is one of the largest all-sky stellar rotation catalogs assembled from a single space mission — covering both hemispheres with uniform photometric quality. The unspotted magnitudes (g_unspotted, bp_unspotted, rp_unspotted) enable spot-corrected photometry, and the per-segment analysis quantifies how rotation periods evolve over the ~34-month Gaia observation baseline.
This dataset is suitable for tabular regression tasks.
Schema
| Column | Type | Description | Sample | Null % |
|---|---|---|---|---|
source_id |
int64 | Gaia DR3 unique source identifier; use for cross-matching with other Gaia tables | 11964580592929024 | 0.0% |
num_segments |
Int32 | Number of time segments used in the rotation analysis; more segments generally improve period reliability | 3 | 0.0% |
num_outliers |
Int32 | Number of photometric outliers excluded from the rotation analysis | 5 | 0.0% |
best_rotation_period |
float64 | Best-estimate stellar rotation period in days; the fundamental scientific output — shorter periods indicate younger, more active stars | 0.993716946166596 | 0.0% |
best_rotation_period_error |
float64 | Formal uncertainty on the best-estimate rotation period (days) | 0.00014861194 | 0.0% |
g_unspotted |
float64 | Estimated unspotted G-band apparent magnitude (brightness the star would have without starspots) | 14.645926 | 0.0% |
g_unspotted_error |
float64 | Uncertainty on the unspotted G-band magnitude | 0.008685217 | 0.0% |
bp_unspotted |
float64 | Estimated unspotted BP-band (330–680 nm) apparent magnitude | 15.397141 | 10.3% |
bp_unspotted_error |
float64 | Uncertainty on the unspotted BP-band magnitude | 0.0056269383 | 10.3% |
rp_unspotted |
float64 | Estimated unspotted RP-band (630–1050 nm) apparent magnitude | 13.813257 | 11.2% |
rp_unspotted_error |
float64 | Uncertainty on the unspotted RP-band magnitude | 0.0035023768 | 11.2% |
max_activity_index_g |
float64 | Maximum G-band activity index across all time segments; proxy for spot coverage fraction — larger values indicate more starspot coverage | 0.011081358 | 0.0% |
max_activity_index_g_error |
float64 | Uncertainty on the maximum G-band activity index | 0.0020487746 | 0.0% |
bp_rp_unspotted |
float64 | Unspotted BP-RP color index (bp_unspotted - rp_unspotted); traces stellar temperature — bluer (smaller) values indicate hotter stars | 1.5838839999999994 | 13.4% |
Quick stats
- 83,931 stars with measured rotation periods
- Median rotation period: 2.26 days
- Fraction with period < 10 days (fast rotators): 93.3%
- Median G-band activity index: 0.0263
Usage
from datasets import load_dataset
ds = load_dataset("juliensimon/gaia-dr3-rotation-modulation", split="train")
df = ds.to_pandas()
from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np
ds = load_dataset("juliensimon/gaia-dr3-rotation-modulation", split="train")
df = ds.to_pandas()
# Rotation period histogram (log scale)
fig, ax = plt.subplots(figsize=(10, 6))
ax.hist(df["best_rotation_period"].dropna(), bins=200, log=True, color="steelblue", alpha=0.8)
ax.set_xscale("log")
ax.set_xlabel("Rotation Period (days)")
ax.set_ylabel("Number of Stars")
ax.set_title("Gaia DR3 Stellar Rotation Period Distribution")
plt.tight_layout()
plt.show()
# Activity index vs rotation period (gyrochronology diagram)
mask = df["best_rotation_period"].notna() & df["max_activity_index_g"].notna()
plt.figure(figsize=(10, 6))
plt.hexbin(
np.log10(df.loc[mask, "best_rotation_period"]),
df.loc[mask, "max_activity_index_g"],
gridsize=100, mincnt=1, cmap="hot"
)
plt.colorbar(label="Count")
plt.xlabel("log10(Rotation Period [days])")
plt.ylabel("Max G Activity Index")
plt.title("Stellar Activity vs Rotation Period")
plt.show()
# Fast rotators (young stars)
fast = df[df["best_rotation_period"] < 5]
print(f"Stars with P < 5 days: {len(fast):,}")
print(f"Median activity index (fast rotators): {fast['max_activity_index_g'].median():.4f}")
Data source
https://gea.esac.esa.int/archive/
Related datasets
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About the author
Created by Julien Simon — AI Operating Partner at Fortino Capital. Part of the Space Datasets collection.
Citation
@dataset{gaia_dr3_rotation_modulation,
title = {Gaia DR3 Stellar Rotation Periods},
author = {juliensimon},
year = {2026},
url = {https://huggingface.co/datasets/juliensimon/gaia-dr3-rotation-modulation},
publisher = {Hugging Face}
}