gaia
Gaia DR3 extract indexed at HEALpix level 8 for spectrophotometric calibration.
Open dataset Updated 2026-06-18
What is gaia?
The dataset provides a HEALpix-indexed subset of Gaia DR3 sources selected for spectrophotometric color calibration tasks, with a maximum of 127 brightest entries per cell.
It supports offline and online use with Siril 1.4 and serves researchers needing a reproducible reference catalog for astronomical calibration workflows.
What you can build with gaia
Spectrophotometric calibration pipelines
Build reference-based color calibration routines for astronomical imaging by querying the pre-indexed HEALpix cells and brightest sources.
Reproducibility benchmarks for Siril
Reproduce and validate photometric results from Siril 1.4 using the exact Gaia DR3 extract provided for scientific verification.
HEALpix-aware ML training subsets
Create spatially balanced training and test splits for models that predict stellar colors or magnitudes from the 127-source-per-cell structure.
Load gaia
Python
from datasets import load_dataset
ds = load_dataset("siril-spcc/gaia")- 1pip install datasets
- 2from datasets import load_dataset
- 3ds = load_dataset('siril-spcc/gaia')
- 4Access the train split and inspect HEALpix indexed columns
- 5Filter cells or magnitude ranges as needed for your calibration task
gaia: pros & cons
Pros
- +HEALpix level-8 indexing for fast spatial queries
- +Capped at 127 brightest sources per cell to control size
- +Directly tied to Siril 1.4 reproducibility needs
- +Ready-to-use Gaia DR3 extract via Hugging Face
Cons
- –Only an extract, not the full Gaia DR3 catalog
- –Optimized specifically for spectrophotometric color calibration
- –May require astronomy domain knowledge to interpret columns
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Frequently asked questions
An extract of Gaia DR3 optimized for spectrophotometric color calibration, indexed at HEALpix level 8 with up to 127 brightest sources per cell.
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