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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
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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

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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