juliensimon/ssodnet-asteroid-properties

SsODNet Asteroid Physical Properties

Part of the Orbital Mechanics Datasets collection on Hugging Face.

Physical and dynamical properties of 1,487,300 asteroids and dwarf planets from the IMCCE (Paris Observatory) Solar System Open Database Network (SsODNet). This is the most comprehensive asteroid characterization catalog available, compiling best estimates from thousands of published studies.

Dataset description

SsODNet aggregates physical property measurements from the astronomical literature into a single, curated "best estimates" flat table (ssoBFT). For each asteroid, IMCCE selects the most reliable published value for each property using a transparent ranking scheme. Properties include diameters, albedos, taxonomic classifications, masses, densities, rotation periods, and thermal inertia — alongside orbital elements and dynamical family memberships.

The fill factor varies by property: orbital elements are available for nearly all objects, while physical measurements like mass (429 objects) and density (468 objects) are known for far fewer.

Schema

Column	Type	Description
sso_id	string	SsODNet unique identifier
sso_number	Int64	IAU asteroid catalog number (null for unnumbered)
sso_name	string	IAU name (null if unnamed)
sso_type	string	Object type (Asteroid, Dwarf Planet, etc.)
sso_class	string	Dynamical class (MB, NEA, Trojan, Centaur, KBO, etc.)
semi_major_axis_au	float64	Orbital semi-major axis (AU)
eccentricity	float64	Orbital eccentricity
inclination_deg	float64	Orbital inclination (degrees)
orbital_period_yr	float64	Orbital period (years)
periapsis_distance_au	float64	Perihelion distance (AU)
apoapsis_distance_au	float64	Aphelion distance (AU)
tisserand_jupiter	float64	Tisserand parameter w.r.t. Jupiter
family_number	Int64	Dynamical family number
family_name	string	Dynamical family name
family_status	string	Family membership status
absolute_magnitude	float64	Absolute magnitude H (best estimate)
absolute_magnitude_err_min	float64	H magnitude lower error bound
absolute_magnitude_err_max	float64	H magnitude upper error bound
diameter_km	float64	Effective diameter (km, best estimate)
diameter_err_min_km	float64	Diameter lower error bound (km)
diameter_err_max_km	float64	Diameter upper error bound (km)
albedo	float64	Geometric albedo (best estimate)
albedo_err_min	float64	Albedo lower error bound
albedo_err_max	float64	Albedo upper error bound
mass_kg	float64	Mass (kg, best estimate)
mass_err_min_kg	float64	Mass lower error bound (kg)
mass_err_max_kg	float64	Mass upper error bound (kg)
density_g_cm3	float64	Bulk density (g/cm3, best estimate)
density_err_min_g_cm3	float64	Density lower error bound (g/cm3)
density_err_max_g_cm3	float64	Density upper error bound (g/cm3)
taxonomy_class	string	Taxonomic class (e.g., S, C, X, V)
taxonomy_complex	string	Taxonomic complex (e.g., S-complex, C-complex)
taxonomy_scheme	string	Classification scheme (Bus-DeMeo, Tholen, etc.)
taxonomy_waverange	string	Wavelength range used for classification
taxonomy_technique	string	Technique used for classification
thermal_inertia	float64	Thermal inertia (J m-2 s-0.5 K-1)
thermal_inertia_err_min	float64	Thermal inertia lower error bound
thermal_inertia_err_max	float64	Thermal inertia upper error bound
rotation_period_h	float64	Rotation period (hours, best estimate)
rotation_period_err_min_h	float64	Rotation period lower error bound (hours)
rotation_period_err_max_h	float64	Rotation period upper error bound (hours)
moid_earth_au	float64	Minimum orbit intersection distance with Earth (AU)

Quick stats

• 1,487,300 asteroids and dwarf planets
• 149,496 with measured diameter
• 149,497 with measured albedo
• 170,933 with taxonomic classification
• 429 with mass estimate
• 468 with density estimate
• 51,005 with rotation period
• 255,987 with dynamical family assignment

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/ssodnet-asteroid-properties", split="train")
df = ds.to_pandas()

# Taxonomy distribution
df["taxonomy_class"].value_counts().head(10)

# Large asteroids with known density
dense = df[df["density_g_cm3"].notna() & (df["diameter_km"] > 100)]
dense[["sso_name", "diameter_km", "density_g_cm3", "taxonomy_class"]].sort_values(
    "diameter_km", ascending=False
)

# Near-Earth asteroids sorted by MOID
neas = df[df["sso_class"] == "NEA"].sort_values("moid_earth_au")
neas[["sso_name", "diameter_km", "moid_earth_au", "albedo"]].head(20)

# Diameter vs albedo by taxonomy
import matplotlib.pyplot as plt
sample = df.dropna(subset=["diameter_km", "albedo", "taxonomy_complex"])
for cpx, grp in sample.groupby("taxonomy_complex"):
    plt.scatter(grp["diameter_km"], grp["albedo"], s=1, alpha=0.4, label=cpx)
plt.xscale("log")
plt.xlabel("Diameter (km)")
plt.ylabel("Albedo")
plt.legend(fontsize=7)

Data source

IMCCE SsODNet — Solar System Open Database Network

The ssoBFT (Best Flat Table) compiles best estimates of physical and dynamical properties for all known asteroids and dwarf planets. Data originates from thousands of peer-reviewed publications, curated by IMCCE (Paris Observatory). See Berthier et al. (2023), "SsODNet: The Solar System Open Database Network", A&A 671, A151.

Pipeline

Source code: juliensimon/space-datasets

Citation

@dataset{ssodnet_asteroid_properties,
  author = {Simon, Julien},
  title = {SsODNet Asteroid Physical Properties},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/ssodnet-asteroid-properties},
  note = {Based on IMCCE SsODNet ssoBFT, Berthier et al. (2023)}
}

License

CC-BY-4.0