juliensimon/gaia-dr3-young-stellar-objects
Gaia DR3 Young Stellar Objects Part of the Astronomy Datasets collection on Hugging Face. The Gaia DR3 young stellar object (YSO) catalog, containing 79,375 YSO candidates identified by the ESA Gaia mission's variability classification pipeline. Each source includes a YSO classification confidence score, variability statistics (amplitudes, standard deviations, skewness, kurtosis), astrometry (positions, parallax, proper motions), and multi-band photometry (G, BP, RP).… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/gaia-dr3-young-stellar-objects.
Gaia DR3 Young Stellar Objects
Part of the [Astronomy Datasets](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743) collection on Hugging Face.
The Gaia DR3 young stellar object (YSO) catalog, containing 79,375 YSO candidates identified by the ESA Gaia mission's variability classification pipeline. Each source includes a YSO classification confidence score, variability statistics (amplitudes, standard deviations, skewness, kurtosis), astrometry (positions, parallax, proper motions), and multi-band photometry (G, BP, RP).
Dataset description
Young stellar objects are pre-main-sequence stars still in the process of forming, often surrounded by circumstellar disks and exhibiting irregular photometric variability. Gaia's all-sky photometric survey identified these candidates through automated variability classification in the vari_classifier_result table. The best_class_score field gives the classifier's confidence for the YSO label (higher = more confident).
This dataset joins three Gaia DR3 tables:
- `vari_classifier_result` — YSO classification and confidence score
- `vari_summary` — variability statistics (mean/median magnitudes, amplitudes, scatter)
- `gaia_source` — astrometry (ra, dec, parallax, proper motion) and catalog photometry
Key columns
Full schema includes 40 columns with variability metrics and photometric parameters.
Quick stats
- 79,375 YSO candidates
- Median G magnitude: 16.99
- Median classification score: 0.512
Usage
from datasets import load_dataset
ds = load_dataset("juliensimon/gaia-dr3-young-stellar-objects", split="train")
df = ds.to_pandas()
# Classification score distribution
print(df["best_class_score"].describe())
# High-confidence YSOs (score > 0.5)
confident = df[df["best_class_score"] > 0.5]
print(f"High-confidence YSOs: {len(confident):,}")
# Color-magnitude diagram
import matplotlib.pyplot as plt
plt.scatter(df["bp_rp"], df["phot_g_mean_mag"], s=1, alpha=0.3, c=df["best_class_score"], cmap="viridis")
plt.colorbar(label="Classification score")
plt.xlabel("BP - RP (mag)")
plt.ylabel("G (mag)")
plt.gca().invert_yaxis()
plt.title("Gaia DR3 YSO Color-Magnitude Diagram")
plt.show()Data source
Gaia Collaboration (2023), Gaia Data Release 3: variability processing and analysis results. European Space Agency. Via ESA Gaia Archive — joined from gaiadr3.vari_classifier_result, gaiadr3.vari_summary, and gaiadr3.gaia_source.
Related datasets
- Gaia DR3 Eclipsing Binaries — Gaia eclipsing binary candidates
- Gaia DR3 Variable Star Summary — all Gaia variable star classifications
Pipeline
Source code: juliensimon/space-datasets
Support
If you find this dataset useful, please give it a ❤️ on the dataset page and share feedback in the Community tab! Also consider giving a ⭐️ to the space-datasets repo.
Citation
@dataset{gaia_dr3_young_stellar_objects,
author = {Simon, Julien},
title = {Gaia DR3 Young Stellar Objects},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/gaia-dr3-young-stellar-objects},
note = {Based on Gaia DR3 (ESA)}
}