juliensimon/gaia-dr3-binary-masses

Gaia DR3 Binary Masses

Credit: NASA/JPL-Caltech

Part of a dataset collection on Hugging Face.

Dataset description

The Gaia DR3 binary masses catalog provides physical masses for binary star systems derived by the ESA Gaia mission. Masses are determined by combining astrometric non-single-star (NSS) solutions with spectroscopic radial velocities and photometric constraints, applying Kepler's third law to the orbital solutions.

These are among the most precisely measured stellar masses available from an all-sky survey, covering ~195,000 systems with primary masses ranging from ~0.1 to more than 10 solar masses. The combination_method column indicates which observational constraints (astrometry, spectroscopy, photometry) were used for each system.

The dataset is scientifically crucial for calibrating stellar evolution models, mass-luminosity relations, and testing stellar structure theory. Direct mass measurements from binary systems are the foundation of our understanding of how stars form, evolve, and die. Unlike masses estimated from stellar models or spectral fitting, these Gaia masses are derived geometrically from orbital dynamics, providing model-independent ground truth across a wide range of stellar types.

The flux ratio (F2/F1 in G-band) constrains the luminosity of the secondary star, enabling estimates of secondary radius and temperature when combined with the primary's known properties. Systems with measured m2 allow direct computation of the mass ratio q = m2/m1, a fundamental parameter in binary evolution theory that controls mass transfer rates, tidal circularization timescales, and ultimate system fate.

This dataset is suitable for tabular regression tasks.

Schema

Column	Type	Description	Sample	Null %
source_id	int64	Gaia DR3 source identifier of the primary star; use for cross-matching with other Gaia tables	33711199137024	0.0%
m1	float64	Primary star mass in solar masses (M☉), derived from orbital solution	0.94867384	0.0%
m1_lower	float64	Lower 1-sigma uncertainty on m1 (M☉)	0.654218	0.0%
m1_upper	float64	Upper 1-sigma uncertainty on m1 (M☉)	0.9998373	0.0%
m2	float64	Secondary component mass in solar masses (M☉); may be NaN if only primary mass is determined	0.5688726	85.3%
m2_lower	float64	Lower 1-sigma uncertainty on m2 (M☉)	0.44135773	0.0%
m2_upper	float64	Upper 1-sigma uncertainty on m2 (M☉)	1.8444434	30.9%
fluxratio	float64	Flux ratio of secondary to primary (F2/F1) in Gaia G-band	-0.026845658	90.1%
fluxratio_lower	float64	Lower uncertainty on flux ratio	0.0	0.2%
fluxratio_upper	float64	Upper uncertainty on flux ratio	0.59	33.0%
combination_method	str	Method used to derive masses: combination of astrometric, spectroscopic, and/or photometric constraints	Orbital+M1	0.0%
m1_ref	str	Reference for the primary mass estimate (short code)	IsocLum	0.0%
mass_ratio	float64	Mass ratio q = m2/m1; computed where both masses are available and m1 > 0	0.45398824218362815	85.3%
m1_uncertainty	float64	Average 1-sigma uncertainty on m1 = (m1_upper + m1_lower) / 2.0 (M☉)	0.82702765	0.0%

Quick stats

• 195,315 binary star systems
• Median primary mass (m1): 1.037 M☉
• Median secondary mass (m2): 0.578 M☉ (where available)
• Systems with measured m2: 28,706 (14.7%)
• Median mass ratio (m2/m1): 0.559

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/gaia-dr3-binary-masses", split="train")
df = ds.to_pandas()

from datasets import load_dataset
import matplotlib.pyplot as plt

ds = load_dataset("juliensimon/gaia-dr3-binary-masses", split="train")
df = ds.to_pandas()

# Primary mass distribution
df["m1"].clip(upper=5).hist(bins=100, log=True)
plt.xlabel("Primary mass m1 (M☉)")
plt.ylabel("Count")
plt.title("Gaia DR3 Binary Star Primary Mass Distribution")
plt.show()

# m1 vs m2 scatter (systems with both measured)
has_m2 = df.dropna(subset=["m1", "m2"])
plt.hexbin(has_m2["m1"], has_m2["m2"], gridsize=80, mincnt=1, cmap="viridis")
plt.colorbar(label="Count")
plt.xlabel("Primary mass m1 (M☉)")
plt.ylabel("Secondary mass m2 (M☉)")
plt.title("Gaia DR3 Binary Mass Pairs")
plt.show()

# Mass ratio distribution
df["mass_ratio"].clip(0, 2).hist(bins=100)
plt.xlabel("Mass ratio q = m2/m1")
plt.ylabel("Count")
plt.title("Binary Mass Ratio Distribution")
plt.show()

Data source

https://gea.esac.esa.int/archive/

Related datasets

• juliensimon/gaia-dr3-spectroscopic-binaries

• juliensimon/gaia-dr3-eclipsing-binaries

• juliensimon/wds

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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_binary_masses,
  title = {Gaia DR3 Binary Masses},
  author = {juliensimon},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/gaia-dr3-binary-masses},
  publisher = {Hugging Face}
}

License

CC-BY-4.0