juliensimon/sdss-asteroid-taxonomy

SDSS-based Asteroid Taxonomy

Part of the Orbital Mechanics Datasets collection on Hugging Face.

Compositional taxonomy for 107,466 SDSS photometric observations of 63,468 asteroids, classified using the scheme of Carvano et al. (2010). Each observation includes SDSS u'g'r'i'z' log-reflectances, a taxonomic class assignment, and a probability score. Orbital elements from the asteroid catalog are merged in for asteroids with known orbits.

Dataset description

The Sloan Digital Sky Survey (SDSS) Moving Object Catalog observed over 100,000 asteroids in five photometric bands (u', g', r', i', z') between 1998 and 2007. Carvano et al. (2010) developed a probabilistic taxonomic classification scheme based on SDSS colors, assigning each observation to one of nine primary compositional classes inspired by the Bus taxonomy:

• V — Basaltic (V-type)
• O — Olivine-rich (O-type)
• Q — Ordinary chondrite-like (Q-type)
• S — Silicaceous (S-complex)
• A — Strongly reddened (A-type)
• L — Moderately reddened (L-type)
• D — Very red, organic-rich (D-type)
• X — Degenerate featureless (X-complex)
• C — Carbon-rich, featureless (C-complex)

When an observation falls near a class boundary, a two-letter compound class is assigned (e.g., SQ, CX, LS) indicating ambiguity between the two types. Each observation receives a probability score (0--100) for the assigned class.

The classification column gives the best overall class per asteroid (from the asteroid summary table), chosen as either the most frequent or highest-scoring class across all SDSS observations of that object.

Schema

Column	Type	Description
object_id	string	Primary identifier (asteroid number or provisional designation)
ast_number	Int64	IAU asteroid catalog number (null for unnumbered)
ast_name	string	IAU asteroid name (null if unnamed)
prov_desig	string	Provisional designation at discovery
tax_class	string	Taxonomic class for this observation (C/S/V/Q/D/L/X/A/O or compound e.g. SQ/CX)
score	Int64	Probability score for assigned class (0-100)
moid	string	Unique SDSS moving-object observation ID
bad_flag	Int64	1 if any magnitude uncertainty exceeds 3rd quartile
log_refl_u	float64	Log reflectance, SDSS u' band
log_refl_err_u	float64	Uncertainty of u' log reflectance
log_refl_g	float64	Log reflectance, SDSS g' band (reference = 1.0)
log_refl_err_g	float64	Uncertainty of g' log reflectance
log_refl_r	float64	Log reflectance, SDSS r' band
log_refl_err_r	float64	Uncertainty of r' log reflectance
log_refl_i	float64	Log reflectance, SDSS i' band
log_refl_err_i	float64	Uncertainty of i' log reflectance
log_refl_z	float64	Log reflectance, SDSS z' band
log_refl_err_z	float64	Uncertainty of z' log reflectance
classification	string	Best overall class for this asteroid (most frequent or highest score)
score_best	Int64	Probability score for best classification
n_class	Int64	Number of classified SDSS observations for this asteroid
method	Int64	1 = most frequent class chosen, 0 = highest score chosen
sequence	string	Sequence of per-observation class assignments
abs_mag_h	float64	Absolute magnitude H from SDSS MOC
proper_semimajor_au	float64	Proper semi-major axis (AU), null if unavailable
proper_eccentricity	float64	Proper eccentricity, null if unavailable
sin_proper_inclination	float64	Sine of proper inclination, null if unavailable
osc_semimajor_au	float64	Osculating semi-major axis (AU)
osc_eccentricity	float64	Osculating eccentricity
osc_inclination_deg	float64	Osculating inclination (degrees)

Quick stats

• 107,466 observations of 63,468 unique asteroids
• 33 taxonomic classes
• Top classes: C (28,784), S (25,713), LS (8,956), L (8,129), X (6,003)
• 78,180 observations with orbital elements

Usage

from datasets import load_dataset

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

# Class distribution
df["tax_class"].value_counts().plot.bar()

# Taxonomic composition vs semi-major axis (main belt structure)
import matplotlib.pyplot as plt
belt = df[df["osc_semimajor_au"].between(2.0, 3.5)]
for cls in ["S", "C", "X", "V"]:
    subset = belt[belt["tax_class"] == cls]
    plt.hist(subset["osc_semimajor_au"], bins=100, alpha=0.5, label=cls, density=True)
plt.xlabel("Semi-major axis (AU)")
plt.legend()

# High-confidence V-type (basaltic) asteroids
vesta_family = df[(df["tax_class"] == "V") & (df["score"] > 80)]

# SDSS color-color diagram
plt.scatter(df["log_refl_r"] - df["log_refl_i"],
            df["log_refl_g"] - df["log_refl_r"],
            c=df["tax_class"].astype("category").cat.codes, s=0.2, alpha=0.3)

Data source

PDS Small Bodies Node — SDSS-based Asteroid Taxonomy V1.1

Based on SDSS Moving Object Catalog observations (1998-2007). See Carvano et al. (2010) and Ivezic et al. (2010).

Pipeline

Source code: juliensimon/space-datasets

Support

If you find this dataset useful, please give it a ❤️ on the dataset page and share feedback in the Community tab! Also consider giving a ⭐️ to the space-datasets repo.

Citation

@dataset{sdss_asteroid_taxonomy,
  author = {Simon, Julien},
  title = {SDSS-based Asteroid Taxonomy},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/sdss-asteroid-taxonomy},
  note = {Based on Carvano et al. (2010) SDSS taxonomy from the PDS Small Bodies Node}
}

License

CC-BY-4.0