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juliensimon/gswlc-galaxy-properties

GSWLC-2 Galaxy Properties Part of the Astronomy Datasets collection on Hugging Face. 659,229 galaxies with physical properties derived from UV-to-infrared spectral energy distribution (SED) fitting. GSWLC-2 (GALEX-SDSS-WISE Legacy Catalog 2) combines ultraviolet photometry from GALEX, optical photometry from SDSS, and mid-infrared photometry from WISE to estimate stellar masses, star formation rates, and dust attenuation for galaxies at redshifts 0.01 < z < 0.30.… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/gswlc-galaxy-properties.

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Dataset Card

GSWLC-2 Galaxy Properties

Part of the [Astronomy Datasets](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743) collection on Hugging Face.

659,229 galaxies with physical properties derived from UV-to-infrared spectral energy distribution (SED) fitting. GSWLC-2 (GALEX-SDSS-WISE Legacy Catalog 2) combines ultraviolet photometry from GALEX, optical photometry from SDSS, and mid-infrared photometry from WISE to estimate stellar masses, star formation rates, and dust attenuation for galaxies at redshifts 0.01 < z < 0.30.

Dataset description

The GSWLC is the definitive catalog for physical properties of low-redshift galaxies, covering ~90% of the SDSS spectroscopic footprint. Version 2 (Salim et al. 2018) incorporates WISE mid-IR photometry to better constrain dust-obscured star formation. The "X" variant (GSWLC-X2) is the master catalog that selects the deepest available UV observation for each galaxy from the A (shallow), M (medium), and D (deep) sub-catalogs.

Physical properties are derived using the CIGALE SED fitting code with Bayesian estimation of stellar mass, star formation rate, and dust attenuation.

Quick stats

  • —659,229 galaxies in the catalog
  • —650,599 with valid stellar mass estimates
  • —650,599 with valid SFR estimates
  • —340,258 classified as star-forming (log sSFR > -11)
  • —310,341 classified as quiescent
  • —Median stellar mass: 10^{10.81} solar masses
  • —Median redshift: 0.1039

Schema

ColumnTypeDescription
objidint64SDSS photometric object ID
glxidint64GALEX photometric ID (null if no UV match)
plateint64SDSS spectroscopic plate number
mjdint64SDSS spectroscopic plate date (MJD)
fiber_idint64SDSS spectroscopic fiber ID
rafloat64Right Ascension (J2000, degrees)
decfloat64Declination (J2000, degrees)
redshiftfloat64Spectroscopic redshift from SDSS
chi2_rfloat64Reduced chi-squared of SED fit
log_mstarfloat64Log stellar mass (solar masses)
log_mstar_errfloat64Error on log stellar mass
log_sfr_sedfloat64Log UV/optical SFR (solar masses/yr)
log_sfr_sed_errfloat64Error on log SFR
a_fuvfloat64Dust attenuation in rest-frame FUV (mag)
a_fuv_errfloat64Error on A_FUV
a_bfloat64Dust attenuation in rest-frame B band (mag)
a_b_errfloat64Error on A_B
a_vfloat64Dust attenuation in rest-frame V band (mag)
a_v_errfloat64Error on A_V
flag_sedint64SED fitting flag (0=OK, 1=broad-line, 2=chi2>30, 5=missing photometry)
uv_surveyint64UV survey depth (1=shallow/A, 2=medium/M, 3=deep/D)
flag_uvint64UV detection flag (0=none, 1=FUV only, 2=NUV only, 3=both)
flag_midirint64Mid-IR flag (0=none, 1=12um, 2=22um, 5=AGN-corrected)
flag_mgsint64SDSS Main Galaxy Sample flag (0=no, 1=yes)
log_ssfrfloat64Derived: log specific SFR (log SFR - log M*, yr^-1)
is_star_formingboolDerived: log sSFR > -11
uv_survey_namestringDerived: human-readable UV survey name

Usage

python
from datasets import load_dataset

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

# Star-forming galaxies
sf = df[df["is_star_forming"]]

# Massive quiescent galaxies
massive_quiescent = df[(df["log_mstar"] > 11) & (~df["is_star_forming"])]

# Star formation main sequence
import matplotlib.pyplot as plt
valid = df[df["log_sfr_sed"].notna() & df["log_mstar"].notna()]
plt.hexbin(valid["log_mstar"], valid["log_sfr_sed"], gridsize=100, mincnt=1)
plt.xlabel("log M* (Msun)")
plt.ylabel("log SFR (Msun/yr)")
plt.title("Star Formation Main Sequence")

# Dusty galaxies (high FUV attenuation)
dusty = df[df["a_fuv"] > 3.0]

# Cross-match with SDSS using objid

Data source

GSWLC-2 — Salim et al. (2016, 2018).

  • —Salim et al. (2016), "GALEX-SDSS-WISE Legacy Catalog (GSWLC): Star Formation Rates, Stellar Masses, and Dust Attenuations of 700,000 Low-Redshift Galaxies", ApJS, 227, 2. arXiv:1610.00712
  • —Salim et al. (2018), "Dust Attenuation Curves in the Local Universe: Demographics and New Laws for Star-forming Galaxies and High-redshift Analogs", ApJ, 859, 11. arXiv:1804.05850

Related datasets

Pipeline

Source code: juliensimon/space-datasets

Citation

bibtex
@dataset{gswlc_galaxy_properties,
  author = {Simon, Julien},
  title = {GSWLC-2 Galaxy Properties},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/gswlc-galaxy-properties},
  note = {Based on GSWLC-2 data (Salim et al. 2016, ApJS 227, 2; Salim et al. 2018, ApJ 859, 11)}
}

License

CC-BY-4.0