OmniSky: 1.58 Million Pre-Cross-Matched Astronomical Objects from 12 Surveys

The first publicly available, pre-cross-matched astronomical dataset that unifies all three modality types -- spectra, light curves, and images -- into a single table. 1,580,216 objects across three populations (stars, galaxies, AGN) are joined from 12 major surveys spanning UV through mid-infrared. Each row is one physical object with all available observations already paired.

Why This Dataset?

Existing multimodal astronomical datasets either provide raw survey collections that users must cross-match themselves, or cover only 1-2 modalities for a single population:

Dataset	Objects	Modalities	Surveys	Populations	Pre-joined?
OmniSky (this)	1.58M	Spectra + Light Curves + Images	12	Stars, Galaxies, AGN	Yes
Multimodal Universe	100M+ (separate)	Spectra + LC + Images	20+	Mixed	No (raw collections)
AstroCLIP	198k	Spectra + Images	2	Galaxies only	Yes
AstroM3	21k	Spectra + LC + Metadata	6	Variable stars only	Yes
DESI/HSC	19k	Spectra + Images	2	Galaxies only	Yes

OmniSky is ready for multimodal representation learning, transfer learning across wavelengths, population classification, and any task requiring multiple views of the same astronomical object in a single row.

Example Objects

Star: 2M21342357+1215247 (Teff=5850 K) — APOGEE IR spectrum + GALEX UV + 2MASS near-IR images:

Star: 2M03324489+4623388 — APOGEE spectrum + 659-epoch ZTF multi-band light curve:

AGN: 001641.39+312612.6 (z=0.948) — SDSS optical spectrum + GALEX UV + WISE mid-IR images:

Dataset at a Glance

Modality coverage by population and survey. Stars have near-complete spectral and image coverage; galaxies and AGN are image-dominated.

Hertzsprung-Russell diagram for 100,000 stars with Gaia photometry. Clean main sequence, red giant branch, and red clump confirm correct Gaia-APOGEE cross-matching.

Match separation distributions for all cross-matched surveys. Sharp peaks near 0" with no flat component confirm matches are real associations, not random.

Sky coverage per survey. Each survey's footprint matches its known observational coverage.

Median stacked spectra per population. APOGEE stars show absorption features; SDSS galaxies and AGN show expected spectral shapes.

Effective temperature cross-validation: APOGEE (high-res IR spectroscopy) vs Gaia GSP-Phot (low-res photometry). Tight 1:1 core with known Gaia failures at low Galactic latitude (blue points in right panel).

Distribution of modality types per object. 78% of stars have >= 2 modality types; 22% have all three (spectra + light curves + images).

---

Quick Start

Installation

pip install datasets numpy pandas pyarrow
# Optional for visualization:
pip install matplotlib astropy

Load the dataset

from datasets import load_dataset
import numpy as np

# Stream without downloading everything (recommended)
ds = load_dataset("kshitijd/omnisky", streaming=True)

# Or download fully (~244 GB)
ds = load_dataset("kshitijd/omnisky")

Get a star with an infrared spectrum

row = ds["train"][0]
if row["population"] == "star" and row["apogee_flux"] is not None:
    flux = np.array(row["apogee_flux"], dtype=np.float32)       # (7514,) normalized IR spectrum
    flux_err = np.array(row["apogee_flux_err"], dtype=np.float32)
    print(f"APOGEE spectrum: {flux.shape}, median flux = {np.median(flux):.3f}")

Get an image cutout (important: reconstruction step)

Image columns are stored as nested lists representing 64x64 pixel arrays. When loaded from Parquet, they appear as 1D arrays of lists. Reconstruct with .tolist():

# Correct way to load images:
if row["twomass_j"] is not None:
    img = np.array(row["twomass_j"].tolist(), dtype=np.float32)  # (64, 64)
    print(f"2MASS J-band: shape={img.shape}, range=[{img.min():.1f}, {img.max():.1f}]")

Plot a 2MASS JHK composite image

import matplotlib.pyplot as plt
import numpy as np

row = ds["train"][0]
fig, axes = plt.subplots(1, 3, figsize=(9, 3))
for ax, band, label in zip(axes, ["twomass_j", "twomass_h", "twomass_k"], ["J", "H", "K"]):
    if row[band] is not None:
        img = np.array(row[band].tolist(), dtype=np.float32)
        ax.imshow(img, origin="lower", cmap="viridis")
        ax.set_title(f"2MASS {label}")
    ax.axis("off")
plt.suptitle(f"{row['object_id']} ({row['population']})")
plt.tight_layout()
plt.savefig("2mass_jhk.png", dpi=150)

Plot GALEX UV images

fig, axes = plt.subplots(1, 2, figsize=(6, 3))
for ax, band, label in zip(axes, ["galex_fuv", "galex_nuv"], ["FUV", "NUV"]):
    if row[band] is not None:
        img = np.array(row[band].tolist(), dtype=np.float32)
        ax.imshow(img, origin="lower", cmap="magma")
        ax.set_title(f"GALEX {label}")
    ax.axis("off")
plt.tight_layout()
plt.savefig("galex_uv.png", dpi=150)

Plot multi-wavelength cutouts side by side (UV to mid-IR)

fig, axes = plt.subplots(1, 7, figsize=(21, 3))
bands = [
    ("galex_fuv", "GALEX FUV", "magma"),
    ("galex_nuv", "GALEX NUV", "magma"),
    ("twomass_j", "2MASS J", "viridis"),
    ("twomass_h", "2MASS H", "viridis"),
    ("twomass_k", "2MASS K", "viridis"),
    ("unwise_w1", "WISE W1", "inferno"),
    ("unwise_w2", "WISE W2", "inferno"),
]
for ax, (col, label, cmap) in zip(axes, bands):
    if row[col] is not None:
        img = np.array(row[col].tolist(), dtype=np.float32)
        ax.imshow(img, origin="lower", cmap=cmap)
        ax.set_title(label, fontsize=9)
    else:
        ax.text(0.5, 0.5, "N/A", ha="center", va="center", transform=ax.transAxes)
    ax.axis("off")
plt.suptitle(f"{row['object_id']} ({row['population']})")
plt.tight_layout()
plt.savefig("multi_wavelength.png", dpi=150)

Plot an APOGEE spectrum

row = ds["train"][0]
if row["apogee_flux"] is not None:
    flux = np.array(row["apogee_flux"], dtype=np.float32)
    plt.figure(figsize=(12, 3))
    plt.plot(flux, lw=0.5, color="navy")
    plt.xlabel("Pixel (7514 good detector pixels)")
    plt.ylabel("Normalized Flux")
    plt.title(f"APOGEE IR Spectrum: {row['object_id']}")
    plt.tight_layout()
    plt.savefig("apogee_spectrum.png", dpi=150)

Plot GALAH spectra (4 separate bands)

fig, axes = plt.subplots(4, 1, figsize=(12, 8), sharex=False)
galah_bands = [
    ("galah_flux_blue", "galah_lambda_blue", "Blue (4713-4903 A)"),
    ("galah_flux_green", "galah_lambda_green", "Green (5648-5873 A)"),
    ("galah_flux_red", "galah_lambda_red", "Red (6478-6737 A)"),
    ("galah_flux_ir", "galah_lambda_ir", "IR (7585-7887 A)"),
]
for ax, (flux_col, lam_col, label) in zip(axes, galah_bands):
    if row[flux_col] is not None and row[lam_col] is not None:
        flux = np.array(row[flux_col], dtype=np.float32)
        lam = np.array(row[lam_col], dtype=np.float32)
        ax.plot(lam, flux, lw=0.5)
        ax.set_ylabel("Flux")
    ax.set_title(f"GALAH {label}", fontsize=9)
ax.set_xlabel("Wavelength (A)")
plt.suptitle(f"GALAH Spectrum: {row['object_id']}")
plt.tight_layout()
plt.savefig("galah_spectrum.png", dpi=150)

Plot a ZTF multi-band light curve

if row["ztf_time"] is not None:
    time = np.array(row["ztf_time"])
    mag = np.array(row["ztf_mag"])
    magerr = np.array(row["ztf_magerr"])
    band = np.array(row["ztf_band"])

    plt.figure(figsize=(10, 4))
    colors = {"zg": "green", "zr": "red", "zi": "orange"}
    for b in np.unique(band):
        mask = band == b
        plt.errorbar(time[mask], mag[mask], yerr=magerr[mask],
                     fmt='.', label=b, color=colors.get(b, "gray"), ms=3, alpha=0.7)
    plt.gca().invert_yaxis()
    plt.xlabel("HJD (Heliocentric Julian Date)")
    plt.ylabel("Magnitude")
    plt.legend()
    plt.title(f"ZTF Light Curve: {row['object_id']}")
    plt.tight_layout()
    plt.savefig("ztf_lightcurve.png", dpi=150)

Plot a TESS light curve

if row["flatiron_tess_time"] is not None:
    time = np.array(row["flatiron_tess_time"], dtype=np.float64)
    flux = np.array(row["flatiron_tess_flux"], dtype=np.float32)

    plt.figure(figsize=(10, 3))
    plt.plot(time, flux, '.', ms=1, alpha=0.5)
    plt.xlabel("BTJD (Barycentric TESS Julian Date)")
    plt.ylabel("Normalized Flux")
    plt.title(f"TESS Light Curve: {row['object_id']}")
    plt.tight_layout()
    plt.savefig("tess_lightcurve.png", dpi=150)

Filter by population

# All stars with spectra AND images
stars = ds["train"].filter(
    lambda x: x["population"] == "star" and x["n_spectra"] > 0 and x["n_images"] > 0
)

# Objects with >= 2 modality types (the cross-modal benchmark subset)
multimodal = ds["train"].filter(lambda x: x["n_modality_types"] >= 2)

# AGN with light curves
agn_variable = ds["train"].filter(
    lambda x: x["population"] == "agn" and x["n_lightcurves"] > 0
)

Use the train/val/test split

# The dataset includes a HEALPix-based spatial split (70/15/15)
train = ds["train"].filter(lambda x: x["split"] == "train")
val = ds["train"].filter(lambda x: x["split"] == "val")
test = ds["train"].filter(lambda x: x["split"] == "test")

Memory-efficient loading with pandas

import pandas as pd
import glob

# Load one shard (~5000 rows, ~400 MB - 1.2 GB depending on population)
df = pd.read_parquet("00000.parquet")

# Load only specific columns (much less RAM)
df = pd.read_parquet("00000.parquet", columns=["object_id", "population", "ra", "dec",
                                                 "apogee_flux", "n_spectra", "n_modality_types"])

# Iterate shard by shard (recommended for large-scale processing)
for f in sorted(glob.glob("*.parquet")):
    chunk = pd.read_parquet(f)
    stars = chunk[chunk["population"] == "star"]
    # process...
    del chunk

Build a PyTorch DataLoader

import torch
from torch.utils.data import Dataset, DataLoader
import pandas as pd
import numpy as np
import glob

class OmniSkyDataset(Dataset):
    """Memory-efficient dataset that loads one shard at a time."""

    def __init__(self, shard_dir, population=None, require_modalities=None):
        self.files = sorted(glob.glob(f"{shard_dir}/*.parquet"))
        self.index = []
        for si, f in enumerate(self.files):
            df = pd.read_parquet(f, columns=["population", "n_spectra", "n_lightcurves", "n_images"])
            for ri in range(len(df)):
                if population and df.iloc[ri]["population"] != population:
                    continue
                if require_modalities:
                    if "spectra" in require_modalities and df.iloc[ri]["n_spectra"] == 0:
                        continue
                    if "images" in require_modalities and df.iloc[ri]["n_images"] == 0:
                        continue
                self.index.append((si, ri))
            del df
        self._cache_si = -1
        self._cache_df = None

    def __len__(self):
        return len(self.index)

    def __getitem__(self, idx):
        si, ri = self.index[idx]
        if si != self._cache_si:
            self._cache_df = pd.read_parquet(self.files[si])
            self._cache_si = si
        row = self._cache_df.iloc[ri]

        sample = {"object_id": row["object_id"], "population": row["population"]}

        # Spectrum
        if row.get("apogee_flux") is not None and isinstance(row["apogee_flux"], (list, np.ndarray)):
            sample["spectrum"] = torch.tensor(np.array(row["apogee_flux"], dtype=np.float32))

        # Image (2MASS J-band) — note .tolist() for 2D reconstruction
        if row.get("twomass_j") is not None and isinstance(row["twomass_j"], (list, np.ndarray)):
            img = np.array(row["twomass_j"].tolist(), dtype=np.float32)
            sample["image"] = torch.tensor(img).unsqueeze(0)  # (1, 64, 64)

        return sample

# Usage
dataset = OmniSkyDataset("./shards/", population="star", require_modalities=["spectra", "images"])
loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=2)

---

Dataset Summary

	Stars	Galaxies	AGN	Total
Count	591,303	238,516	750,397	1,580,216
>= 2 modality types	78%	46%	19%
All 3 modality types	22%	0% (no LC by design)	4%
Median surveys per object	5	2	3

Per-Source Coverage

Stars (591,303)

Source	Column	Coverage
APOGEE DR17	apogee_flux	100%
Gaia DR3 BP/RP	flatiron_gaia_coeff	47%
GALAH DR4	galah_flux_blue	5%
TESS	flatiron_tess_flux	10%
ZTF DR23	ztf_time	13%
2MASS	twomass_j/h/k	100%
GALEX	galex_fuv/nuv	100%
unWISE	unwise_w1/w2	98%

Galaxies (238,516)

Source	Column	Coverage
SDSS DR17	sdss_flux	9%
DESI EDR	flatiron_desi_spectrum_flux	14%
GALEX	galex_fuv/nuv	100%
unWISE	unwise_w1/w2	98%

AGN (750,397)

Source	Column	Coverage
SDSS DR17	sdss_flux	18%
ZTF DR23	ztf_time	9%
GALEX	galex_fuv/nuv	100%
unWISE	unwise_w1/w2	88%

---

Cross-Match Quality

All positional cross-matches use astropy.coordinates.SkyCoord.match_to_catalog_sky() with a 3 arcsecond radius. Catalog positions are propagated to each survey's observation epoch using Gaia DR3 proper motions before matching.

Metric	Value
Median match separation	0.08"
Mean false match rate (shifted-catalog test)	0.02%
Max false match rate (DESI)	0.06%
Stars with proper motion data	99.2%
Median proper motion	7.3 mas/yr

Match separations are stored in {source}_match_sep_arcsec columns so users can apply custom quality cuts.

---

Data Sources

Spectra

Source	Instrument	Wavelength	Resolution	Population
APOGEE DR17	APOGEE (APO + LCO)	1.51--1.70 um (IR)	R ~ 22,500	Stars
Gaia DR3 BP/RP	Gaia BP/RP	330--1050 nm	R ~ 50--100	Stars
GALAH DR4	HERMES (AAT)	4713--7887 A	R ~ 28,000	Stars
SDSS DR17	BOSS / eBOSS	3600--10400 A	R ~ 2000	Galaxies, AGN
DESI EDR	DESI	3600--9800 A	R ~ 2000--5000	Galaxies, AGN

Light Curves

Source	Instrument	Bandpass	Cadence	Population
TESS	TESS	600--1000 nm	2--30 min	Stars, AGN
ZTF DR23	ZTF (Palomar)	g, r, i	1--3 day	Stars, AGN

Images

Source	Instrument	Bands	Pixel Scale	Cutout Size	Population
2MASS	2MASS	J, H, K	~1 arcsec/px	64 x 64	Stars
GALEX	GALEX	FUV, NUV	~1.5 arcsec/px	64 x 64	All
unWISE	WISE	W1, W2	~2.75 arcsec/px	64 x 64	All
Legacy Survey	DECam / Mosaic / 90Prime	g, r, z	0.262 arcsec/px	64 x 64	All

---

Schema

Core columns (all objects)

Column	Type	Description
object_id	string	Unique identifier (APOGEE 2MASS ID for stars, PROVABGS ID for galaxies, SDSS DR16Q name for AGN)
ra	float64	Right ascension (degrees, J2000)
dec	float64	Declination (degrees, J2000)
population	string	"star", "galaxy", or "agn"
pmra	float64	Proper motion in RA (mas/yr, Gaia convention: mu_alpha * cos(dec)). 0 for extragalactic objects.
pmdec	float64	Proper motion in Dec (mas/yr). 0 for extragalactic objects.
n_spectra	int	Count of spectral datasets with data
n_lightcurves	int	Count of light curve datasets with data
n_images	int	Count of image bands with data
n_modality_types	int	Count of modality types with data (0--3: spectra, light curves, images)
split	string	"train" (70%), "val" (15%), or "test" (15%) -- HEALPix spatial split

Spectra columns

Column	Type	Shape	Description
apogee_flux	list[float32]	(7514,)	APOGEE normalized flux, cropped to good detector pixels
apogee_flux_err	list[float32]	(7514,)	APOGEE flux uncertainty
galah_flux_blue	list[float32]	variable	GALAH blue band flux (4713--4903 A)
galah_lambda_blue	list[float32]	variable	GALAH blue band wavelength
galah_flux_green	list[float32]	variable	GALAH green band flux (5648--5873 A)
galah_lambda_green	list[float32]	variable	GALAH green band wavelength
galah_flux_red	list[float32]	variable	GALAH red band flux (6478--6737 A)
galah_lambda_red	list[float32]	variable	GALAH red band wavelength
galah_flux_ir	list[float32]	variable	GALAH IR band flux (7585--7887 A)
galah_lambda_ir	list[float32]	variable	GALAH IR band wavelength
sdss_flux	list[float32]	variable	SDSS/BOSS spectral flux (10^-17 erg/s/cm^2/A)
sdss_loglam	list[float32]	variable	SDSS log10(wavelength / A)
sdss_ivar	list[float32]	variable	SDSS inverse variance
flatiron_gaia_coeff	list[float32]	(110,)	Gaia BP/RP spectral coefficients (requires GaiaXPy to reconstruct)
flatiron_desi_spectrum_flux	list[float32]	variable	DESI coadded spectral flux
flatiron_desi_spectrum_lambda	list[float32]	variable	DESI wavelength array (A)
flatiron_desi_spectrum_ivar	list[float32]	variable	DESI inverse variance

Light curve columns

Column	Type	Shape	Description
ztf_time	list[float64]	variable	ZTF observation times (Heliocentric MJD). Time-sorted, multi-band interleaved.
ztf_mag	list[float32]	variable	ZTF PSF magnitudes
ztf_magerr	list[float32]	variable	ZTF magnitude uncertainties
ztf_band	list[str]	variable	ZTF filter ("zg", "zr", "zi")
flatiron_tess_time	list[float64]	variable	TESS observation times (BTJD)
flatiron_tess_flux	list[float32]	variable	TESS normalized flux
flatiron_tess_flux_err	list[float32]	variable	TESS flux uncertainty

Image columns

All image columns are stored as nested lists representing 64 x 64 pixel cutouts. To reconstruct as a 2D numpy array:

img = np.array(row["twomass_j"].tolist(), dtype=np.float32)  # shape: (64, 64)

Column	Description
twomass_j, twomass_h, twomass_k	2MASS J/H/K near-infrared cutouts
galex_fuv, galex_nuv	GALEX far-UV (1528 A) / near-UV (2271 A) cutouts
unwise_w1, unwise_w2	unWISE W1 (3.4 um) / W2 (4.6 um) mid-infrared cutouts
legacy_g, legacy_r, legacy_z	Legacy Survey optical g/r/z cutouts (very low coverage)

Match quality columns

Column	Description
flatiron_gaia_match_sep_arcsec	Angular separation of Gaia cross-match (arcsec)
flatiron_tess_match_sep_arcsec	Angular separation of TESS cross-match
flatiron_desi_match_sep_arcsec	Angular separation of DESI cross-match
ztf_match_sep_arcsec	Angular separation of ZTF cross-match

Key metadata columns

The dataset includes ~300 metadata columns from source surveys. Key examples:

Column	Description
apogee_teff	APOGEE effective temperature (K)
apogee_logg	APOGEE surface gravity (log g)
flatiron_gaia_phot_g_mean_mag	Gaia G-band apparent magnitude
flatiron_gaia_parallax	Gaia parallax (mas)
flatiron_gaia_bp_rp	Gaia BP-RP color (mag)
flatiron_gaia_teff_gspphot	Gaia photometric effective temperature
flatiron_desi_z	DESI spectroscopic redshift
flatiron_desi_spectype	DESI spectral classification
agn_redshift	AGN redshift from SDSS DR16Q

---

File Format and System Requirements

Format

318 Parquet shard files, up to 5000 rows each. Total on-disk: 244 GB compressed. Populations are interleaved -- filter on population to select types. 354 columns total.

System Requirements

Use Case	RAM	Disk	Notes
HuggingFace streaming	~1 GB	0	No download needed
Load one shard	1--2 GB	1.3 GB	Recommended for most workflows
Load one population	50--100 GB	244 GB	e.g., all 591k stars
Load full dataset	200+ GB	244 GB	Only if you have the RAM

Recommended workflow

For most users, iterate shard by shard or use HuggingFace streaming:

# Streaming (no download)
from datasets import load_dataset
ds = load_dataset("kshitijd/omnisky", streaming=True)
for row in ds["train"]:
    pass  # process row by row

# Or shard by shard (download once)
import pandas as pd
import glob
for f in sorted(glob.glob("path/to/shards/*.parquet")):
    df = pd.read_parquet(f)
    # process...
    del df

---

How It Was Built

Pipeline Overview

Built with a custom Python pipeline on NCSA Delta AI (32 CPUs, 408 GB RAM, NVMe storage). Total runtime: ~16 hours. Open-source pipeline available on GitHub.

Catalog Construction

• Stars (591k): APOGEE DR17 allStar catalog (MAST), filtered to SNR > 50, cross-matched to Gaia DR3 via CDS XMatch (1" radius), deduplicated by APOGEE_ID keeping highest-SNR observation.
• Galaxies (239k): PROVABGS seed catalog from Flatiron Institute (60 HDF5 cells).
• AGN (750k): SDSS DR16Q quasar catalog, filtered to z > 0.01.

Cross-Matching

All positional cross-matches used 3" radius with proper motion epoch propagation. Catalog positions were propagated from Gaia epoch 2016.0 to each survey's observation epoch using Gaia DR3 proper motions (median 7.3 mas/yr, 99.2% availability). Survey epochs: 2MASS (1999.5), GALEX (2007.0), SDSS (2005.0), ZTF (2021.0), TESS (2020.0), DESI (2021.0), unWISE (2014.0).

Match separations stored for every crossmatch. False match rate validated via shifted-catalog experiment (30" offset): 0.02% mean, 0.06% maximum.

Quality Controls

• HDF5 column name normalization (lowercase)
• APOGEE spectra cropped to good detector pixels: np.r_[246:3274, 3585:6080, 6344:8335] (7514 pixels)
• GALAH spectra stored as 4 separate bands (no concatenation across wavelength gaps)
• ZTF light curves time-sorted within each object
• Shard deduplication: closest match kept (sorted by match separation)
• Chunked finalize merge (50k objects at a time) to prevent OOM
• All images stored as numpy arrays, converted to lists only at Parquet write time

---

Data Quality Notes

• APOGEE spectra are continuum-normalized. Flux values are typically 0.5--1.2. Median flux = 1.015.
• Gaia BP/RP is stored as 110 Hermite coefficients (flatiron_gaia_coeff), NOT sampled spectra. Use GaiaXPy to reconstruct.
• TESS light curves have ~53% NaN fraction -- this is normal (data quality flags, gaps between sectors).
• ZTF light curves are time-sorted and multi-band interleaved. Use the ztf_band column to separate bands.
• ZTF times are Heliocentric MJD (HMJD). TESS times are Barycentric TESS Julian Date (BTJD).
• Image cutouts are centered on the epoch-propagated catalog position. Some cutouts may contain NaN pixels at edges.
• Legacy Survey has very low coverage (<1%) and may be dropped in future versions.
• Missing data is stored as None/null for array columns and NaN for scalar columns.

Known Limitations

1. Galaxy spectral coverage is low (~14% DESI, ~9% SDSS). Most PROVABGS galaxies lack spectroscopic observations.
2. No light curves for galaxies by design -- TESS and ZTF are routed to stars and AGN only.
3. Gaia BP/RP coefficients are counted in n_spectra but require reconstruction. Users expecting raw spectra should check column names.
4. AGN sample is large (750k) but spectral coverage is only 18% (SDSS). Most AGN have only images.
5. Selection biases inherited from parent surveys: APOGEE targets bright giants, PROVABGS is in the DESI footprint, DR16Q is spectroscopically confirmed only.
6. ~354 columns -- most are Gaia and DESI metadata. Core science columns are listed in the Schema section.

---

Citation

If you use this dataset, please cite the underlying surveys:

@article{abdurrouf2022,
  title={The Seventeenth Data Release of the Sloan Digital Sky Surveys},
  author={Abdurro'uf and others},
  journal={ApJS},
  volume={259},
  pages={35},
  year={2022}
}

@article{gaia2023,
  title={Gaia Data Release 3: Summary of the content and survey properties},
  author={{Gaia Collaboration}},
  journal={A\&A},
  volume={674},
  pages={A1},
  year={2023}
}

@article{desi2024,
  title={DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars},
  author={{DESI Collaboration}},
  journal={AJ},
  year={2024}
}

@article{bellm2019,
  title={The Zwicky Transient Facility: System Overview, Performance, and First Results},
  author={Bellm, Eric C. and others},
  journal={PASP},
  volume={131},
  pages={018002},
  year={2019}
}

@article{buder2024,
  title={The GALAH Survey: Data Release 4},
  author={Buder, Sven and others},
  journal={arXiv preprint arXiv:2409.19858},
  year={2024}
}

@article{ricker2015,
  title={Transiting Exoplanet Survey Satellite (TESS)},
  author={Ricker, George R. and others},
  journal={JATIS},
  volume={1},
  pages={014003},
  year={2015}
}

License

Released under CC-BY-4.0. The underlying survey data is subject to each survey's individual data use policies.