juliensimon/gaia-dr3-rotation-modulation

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

• juliensimon/gaia-dr3-young-stellar-objects

• juliensimon/gaia-dr3-eclipsing-binaries

• juliensimon/aavso-vsx-variable-stars

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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}
}

License

CC-BY-4.0