Datasets:
image imagewidth (px) 128 128 | simulation stringclasses 3
values | snapshot int32 95 135 | subhalo_id int32 0 800k |
|---|---|---|---|
TNG100 | 99 | 613,338 | |
Illustris | 135 | 379,786 | |
Illustris | 131 | 125,162 | |
Illustris | 135 | 492,067 | |
Illustris | 131 | 487,120 | |
TNG100 | 99 | 334,352 | |
TNG100 | 99 | 299,937 | |
Illustris | 135 | 536,793 | |
Illustris | 131 | 498,601 | |
Illustris | 135 | 462,840 | |
Illustris | 131 | 409,415 | |
Illustris | 131 | 398,079 | |
Illustris | 131 | 528,698 | |
TNG100 | 95 | 513,771 | |
Illustris | 131 | 512,673 | |
TNG100 | 95 | 67 | |
Illustris | 131 | 464,265 | |
Illustris | 135 | 117,370 | |
Illustris | 131 | 80,015 | |
TNG100 | 99 | 406,136 | |
TNG100 | 99 | 539,954 | |
TNG100 | 95 | 577,617 | |
Illustris | 131 | 268,507 | |
Illustris | 135 | 467,718 | |
Illustris | 135 | 486,202 | |
Illustris | 131 | 471,031 | |
TNG100 | 99 | 204,252 | |
Illustris | 131 | 353,061 | |
TNG100 | 95 | 415,646 | |
Illustris | 135 | 468,188 | |
Illustris | 135 | 249,385 | |
Illustris | 131 | 60,335 | |
TNG100 | 95 | 423,798 | |
Illustris | 131 | 491,459 | |
Illustris | 131 | 507,196 | |
TNG100 | 95 | 261,776 | |
Illustris | 131 | 151,700 | |
Illustris | 131 | 533,585 | |
Illustris | 135 | 385,454 | |
Illustris | 135 | 566,076 | |
TNG100 | 99 | 83,331 | |
TNG50 | 95 | 98 | |
TNG100 | 99 | 556,383 | |
Illustris | 135 | 313,543 | |
Illustris | 131 | 477,963 | |
Illustris | 131 | 186,968 | |
TNG100 | 95 | 557,079 | |
Illustris | 135 | 279,108 | |
TNG100 | 99 | 629,208 | |
TNG100 | 95 | 570,092 | |
TNG100 | 95 | 575,557 | |
TNG100 | 99 | 404,217 | |
Illustris | 135 | 401,782 | |
Illustris | 131 | 252,716 | |
Illustris | 131 | 463,007 | |
TNG100 | 95 | 467,632 | |
Illustris | 135 | 15 | |
TNG100 | 99 | 41,652 | |
TNG100 | 95 | 512,644 | |
Illustris | 131 | 447,988 | |
Illustris | 131 | 507,704 | |
Illustris | 131 | 520,542 | |
Illustris | 135 | 433,703 | |
TNG100 | 95 | 363,968 | |
TNG100 | 95 | 400,957 | |
Illustris | 131 | 540,601 | |
TNG100 | 99 | 534,906 | |
Illustris | 135 | 554,737 | |
Illustris | 135 | 542,109 | |
Illustris | 131 | 587,854 | |
Illustris | 135 | 480,846 | |
TNG100 | 99 | 409,762 | |
TNG100 | 95 | 566,050 | |
TNG100 | 95 | 101,353 | |
TNG100 | 95 | 475,326 | |
TNG100 | 95 | 553,281 | |
TNG100 | 95 | 466,651 | |
Illustris | 135 | 517,031 | |
Illustris | 131 | 536,950 | |
TNG100 | 99 | 17,245 | |
TNG100 | 99 | 346,422 | |
TNG50 | 99 | 455,291 | |
TNG100 | 99 | 556,962 | |
Illustris | 131 | 319,984 | |
TNG100 | 95 | 482,570 | |
TNG100 | 95 | 572,938 | |
Illustris | 135 | 110,591 | |
Illustris | 135 | 474,041 | |
Illustris | 131 | 143,671 | |
TNG100 | 99 | 424,154 | |
Illustris | 131 | 354,271 | |
TNG100 | 95 | 718,136 | |
TNG100 | 99 | 510,883 | |
TNG100 | 95 | 622,145 | |
Illustris | 135 | 446,666 | |
Illustris | 131 | 46,613 | |
Illustris | 131 | 74,663 | |
Illustris | 131 | 217,310 | |
TNG100 | 99 | 586,229 | |
Illustris | 131 | 522,193 |
IllustrisTNG SKIRT SDSS
Preprocessed synthetic galaxy images derived from the IllustrisTNG cosmological simulations. Raw multi-band FITS images were produced with the SKIRT Monte Carlo radiative transfer code in SDSS photometric bands and subsequently processed into 128 × 128 RGB images ready for machine-learning applications.
The dataset is designed as training data for the Spherinator / HiPSter framework, but it is general-purpose and suitable for any galaxy-morphology task.
Dataset Details
Dataset Description
Each sample represents a single galaxy subhalo rendered as an observer-frame RGB image.
Starting from the raw FITS images a deterministic preprocessing pipeline is applied:
| Step | Description |
|---|---|
| RGB colour creation | Combines four SDSS bands into three channels with per-channel scalers and an arcsinh stretch |
| Unhealthy-data filter | Removes images containing NaN / Inf values or constant pixel content |
| Truncation filter | Discards galaxies whose major axis is larger than 40 % of the image extent |
| Horizontal alignment | Rotates each galaxy so that the morphological major axis is horizontal |
| Inclination filter | Discards galaxies whose apparent minor to major axis ratio is < 0.5 (near face-on) |
| Crop | Centre-crops to 50 % of the image extent |
| Resize | Bicubic resize to 128 × 128 pixels |
| Reflectional invariance | Canonicalises handedness so the bright side is always on the left |
| Gaussian blur | 3 × 3 kernel smoothing |
| Circular mask | Pixels outside the inscribed circle are set to zero |
| Min–max normalisation | Pixel values rescaled to [0, 1] per image |
- Curated by: HITS-AIN
- Source simulation: IllustrisTNG public data release
- Details on the production of the images: https://www.tng-project.org/data/docs/specifications/#sec5l
- Original publication (for the images): https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.4140R/abstract
- Radiative transfer: SKIRT
- Preprocessing tool: PEST
- License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Dataset Structure
Data Fields
| Field | Type | Description |
|---|---|---|
image |
Image (128 × 128, RGB, uint8) |
Preprocessed galaxy image |
simulation |
string |
IllustrisTNG run identifier (e.g. TNG100-1) |
snapshot |
int32 |
Simulation snapshot number |
subhalo_id |
int32 |
Subhalo ID within the Friends-of-Friends group catalogue |
Data Splits
The dataset ships as a single train split.
Dataset Creation
Source Data
Raw FITS files were downloaded from the IllustrisTNG public data release. Each file contains a multi-band image cube with four SDSS photometric bands (u, g, r, i) generated by SKIRT radiative transfer post-processing of IllustrisTNG snapshots.
File-path convention used to extract metadata:
<root>/<simulation>/sdss/snapnum_<snapshot>/data/broadband_<subhalo_id>.fits
Data Collection and Processing
Preprocessing was performed with
PEST using the pipeline defined in
pipelines/illustris_skirt.yaml.
Citation
If you use this dataset, please cite the IllustrisTNG public data release and the PEST/Spherinator paper:
@article{Nelson2019,
author = {Nelson, D. and Springel, V. and Pillepich, A. and others},
title = {The IllustrisTNG simulations: public data release},
journal = {Computational Astrophysics and Cosmology},
year = {2019},
volume = {6},
pages = {2},
doi = {10.1186/s40668-019-0028-x}
}
@article{Polsterer2024,
author = {Polsterer, Kai Lars and Doser, Bernd and Fehlner, Andreas and Trujillo-Gomez, Sebastian},
title = {{Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations}},
url = {https://arxiv.org/abs/2406.03810},
doi = {10.48550/arXiv.2406.03810},
year = {2024}
}
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