| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-regression |
| - feature-extraction |
| language: |
| - en |
| tags: |
| - astronomy |
| - desi |
| - provabgs |
| - galaxies |
| - multimodal |
| - legacysurvey |
| - galaxy-parameters |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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)` |
|
|
