Datasetpublic

BrunoBarreto/sdss_dr12_stars_regression

SDSS DR12 Stellar Spectra Parquet Benchmark This dataset provides a fixed supervised benchmark split for stellar atmospheric parameter estimation from SDSS DR12 optical spectra. It contains three Parquet files: train.parquet validation.parquet test.parquet The split sizes are: Split Number of spectra Train 29,422 Validation 4,846 Test 14,443 Each row corresponds to one SDSS stellar spectrum and includes raw spectral arrays, processed spectral features… See the full description on the dataset page: https://huggingface.co/datasets/BrunoBarreto/sdss_dr12_stars_regression.

sourceHugging Facecc-by-4.0updated 2mo agoView on Hugging Face

0likes38downloads

Dataset Card

SDSS DR12 Stellar Spectra Parquet Benchmark

This dataset provides a fixed supervised benchmark split for stellar atmospheric parameter estimation from SDSS DR12 optical spectra.

It contains three Parquet files:

text

train.parquet
validation.parquet
test.parquet

The split sizes are:

Split	Number of spectra
Train	29,422
Validation	4,846
Test	14,443

Each row corresponds to one SDSS stellar spectrum and includes raw spectral arrays, processed spectral features, source identifiers, basic metadata, and catalog stellar-parameter labels with uncertainties.

Files

`train.parquet`

Training split used for model fitting.

`validation.parquet`

Validation split used for model selection and hyperparameter tuning.

`test.parquet`

Held-out test split used only for final evaluation.

Columns

Column	Description
`spec_id`	Internal spectrum identifier used to align the split with the downloaded FITS files
`plate`	SDSS plate identifier
`mjd`	Modified Julian Date of the observation
`fiber`	SDSS fiber identifier
`ra`	Right ascension in degrees
`dec`	Declination in degrees
`snr`	Signal-to-noise ratio from the source catalog, when available
`rv_adop`	Adopted radial velocity from the source catalog
`rv_adop_unc`	Uncertainty of the adopted radial velocity
`flux`	Raw SDSS flux array from the FITS spectrum
`loglam`	Log10 wavelength array corresponding to `flux`
`ivar`	Inverse variance array from the FITS spectrum
`mask`	Boolean mask where invalid or non-positive-inverse-variance pixels are marked
`processed_flux`	Fixed-length processed spectral feature vector used by the benchmark models
`catalog_teff`	Adopted catalog effective temperature
`catalog_teff_unc`	Uncertainty of `catalog_teff`
`catalog_feh`	Adopted catalog metallicity `[Fe/H]`
`catalog_feh_unc`	Uncertainty of `catalog_feh`
`catalog_logg`	Adopted catalog surface gravity
`catalog_logg_unc`	Uncertainty of `catalog_logg`

Target Labels

The supervised regression targets are the adopted catalog stellar parameters:

Target	Description	Unit
`catalog_teff`	Effective temperature	K
`catalog_feh`	Metallicity relative to solar	dex
`catalog_logg`	Surface gravity	dex

The corresponding uncertainty columns are:

text

catalog_teff_unc
catalog_feh_unc
catalog_logg_unc

These are taken from the catalog uncertainty fields:

text

TEFF_ADOP_UNC
FEH_ADOP_UNC
LOGG_ADOP_UNC

Loading the Dataset

python

from datasets import load_dataset

dataset = load_dataset("BrunoBarreto/sdss_dr12_stars_regression")

train = dataset["train"]
validation = dataset["validation"]
test = dataset["test"]

print(train[0].keys())

Example: Training on Processed Flux

python

import numpy as np

from sklearn.linear_model import RidgeCV
from sklearn.metrics import mean_absolute_error

X_train = np.stack(train["processed_flux"]).astype("float32")
X_test = np.stack(test["processed_flux"]).astype("float32")

y_train = np.array(
    [
        train["catalog_teff"],
        train["catalog_feh"],
        train["catalog_logg"],
    ],
    dtype="float32",
).T

y_test = np.array(
    [
        test["catalog_teff"],
        test["catalog_feh"],
        test["catalog_logg"],
    ],
    dtype="float32",
).T

alphas = np.logspace(-3, 3, 13)

print("Ridge ...")
model = RidgeCV(alphas=alphas)
model.fit(X_train, y_train)

pred = model.predict(X_test)

mae = mean_absolute_error(y_test, pred, multioutput="raw_values")

print(f"MAE Teff:   {mae[0]:.2f} K")
print(f"MAE [Fe/H]: {mae[1]:.3f} dex")
print(f"MAE logg:   {mae[2]:.3f} dex")

The raw arrays can be used to build custom preprocessing pipelines or to feed models that require native observed-frame spectra.

Paper: arxiv.org/abs/2606.13868

How to cite this dataset

If you use this dataset in your research, please cite it as:

bibtex

@misc{Barreto2026_SDSSDR12StarsRegression,
  author       = {Barreto, Bruno Santos Meneses and Eisencraft, Marcio},
  title        = {{SDSS DR12 Stellar Spectra}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {Hugging Face dataset},
  doi          = {10.57967/hf/9425},
  url          = {https://huggingface.co/datasets/BrunoBarreto/sdss_dr12_stars_regression}
}