OneAstronomy/provabgs-galaxy-estimation

PROVABGS desi_legacy_fusion Dataset

Summary

109,991 BGS galaxies with multimodal data from DESI (spectra + photometry) × Legacy Survey (imaging + photometry), matched within 1 arcsec.

Split	Samples
train	87,992
validation	10,999
test	11,000
total	109,991

Labels (Galaxy Parameters from PROVABGS SED fitting)

Column	Description	Units
z_hp	Redshift	—
log_mstar	Stellar mass	log(M☉)
tage_mw	Mass-weighted age	Gyr
log_z_mw	Metallicity log₁₀(Z_MW)	log(Z)
log_ssfr	Specific SFR log₁₀(SFR/M★)	log(yr⁻¹)

Modalities

Column	Shape	Description
image_pixels_raw	(102400,)	LS image (4, 160, 160), des-g/r/i/z, flat float32, nanomaggies
spectrum_flux_raw	(7781,)	DESI-BGS spectrum, float32
spectrum_ivar	(7781,)	Inverse variance, float32
spectrum_mask	(7781,)	Bad pixel mask, bool
ls_flux_g/r/z/w1/w2	scalar	Legacy Survey photometry, float32
desi_flux_g/r/z/w1/w2	scalar	DESI photometry, float32

Wavelength grid in wavelength_grid.json, image channel layout in image_shape.json.

Quick Start

from datasets import load_dataset
import numpy as np

BASE = "/mnt/si0009256k6u/ckdata/aiready/provabgs/hf_dataset"
ds = load_dataset("parquet", data_dir=BASE, streaming=True)

for sample in ds["train"].with_format(type="numpy").take(10):
    img  = sample["image_pixels_raw"].reshape(4, 160, 160)  # float32
    spec = sample["spectrum_flux_raw"]                       # (7781,) float32
    z    = sample["z_hp"]
    logM = sample["log_mstar"]
    age  = sample["tage_mw"]
    ssf  = sample["log_ssfr"]

Notes

• row_group_size=100 for efficient streaming
• List columns stored as float32 (not float64)
• Normalization deferred to training pipeline
• log_ssfr = log10(AVG_SFR) - log_mstar
• log_z_mw = log10(Z_MW)