metadata

license: cc-by-sa-4.0
size_categories:
  - 1M<n<10M
dataset_info:
  - config_name: default
    features:
      - name: image
        dtype: image
      - name: image_crop
        dtype: image
      - name: dr8_id
        dtype: string
      - name: galaxy_size
        dtype: int64
    splits:
      - name: test
        num_bytes: 12057249781.25
        num_examples: 86471
      - name: validation
        num_bytes: 12065699086.25
        num_examples: 86499
      - name: train
        num_bytes: 1181934533243.5
        num_examples: 8474566
    download_size: 1206114375284
    dataset_size: 1206057482111
  - config_name: v1.0
    features:
      - name: image
        dtype: image
      - name: dr8_id
        dtype: string
    splits:
      - name: train
        num_bytes: 959387460144.3469
        num_examples: 8474566
      - name: test
        num_bytes: 9785671701.822557
        num_examples: 86471
      - name: validation
        num_bytes: 9798204502.80013
        num_examples: 86499
    download_size: 982501453040
    dataset_size: 978971336348.9696
configs:
  - config_name: default
    data_files:
      - split: test
        path: with_crops/test-*
      - split: validation
        path: with_crops/validation-*
      - split: train
        path: with_crops/train-*
  - config_name: v1.0
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*
tags:
  - astronomy

Galaxies for training astroPT

Here we have ~8.5 million galaxy cutouts from the DESI legacy survey DR8. The cut outs are 512x512 pixel jpg images centred on the galaxy source.

I've split away 1% of the images into a test set, and 1% into a validation set. The remaining 98% of the images comprise the training set.

There is also accompanying metadata! To combine the metadata with the galaxy images you can do (for example):

from datasets import load_dataset, concatenate_datasets

# Load the `galaxies' dataset with metadata
galaxies = load_dataset("Smith42/galaxies", streaming=True)
metadata = load_dataset("Smith42/galaxies_metadata", streaming=True).remove_columns("dr8_id")
combined = concatenate_datasets([galaxies['train'], metadata['train']], axis=1)

The metadata is also available in parquet format in the root dir of this repo. You can link the metadata with the galaxies via their dr8_id.

Useful links

Paper here: https://arxiv.org/abs/2405.14930

Models here: https://huggingface.co/Smith42/astroPT

Code here: https://github.com/smith42/astroPT

Upstream catalogue is on Zenodo and paper describing the catalogue is available as Walmsley+2023.

If you find this dataset useful please consider citing the sources below 🚀🚀:

@article{ref_dey2019,
    author = {Dey, A. and Schlegel, D. J. and Lang, D. and Blum, R. and Burleigh, K. and Fan, X. and Findlay, J. R. and Finkbeiner, D. and Herrera, D. and Juneau, S. and others},
    title = {{Overview of the DESI Legacy Imaging Surveys}},
    journal = {Astronomical Journal},
    volume = {157},
    number = {5},
    pages = {168},
    year = {2019},
    issn = {1538-3881},
    publisher = {The American Astronomical Society},
    doi = {10.3847/1538-3881/ab089d}
}

@article{ref_walmsley2023,
    author = {Walmsley, M. and G{\ifmmode\acute{e}\else\'{e}\fi}ron, T. and Kruk, S. and Scaife, A. M. M. and Lintott, C. and Masters, K. L. and Dawson, J. M. and Dickinson, H. and Fortson, L. and Garland, I. L. and others},
    title = {{Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys}},
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {526},
    number = {3},
    pages = {4768--4786},
    year = {2023},
    issn = {0035-8711},
    publisher = {Oxford Academic},
    doi = {10.1093/mnras/stad2919}
}

@article{ref_smith2024,
    author = {Smith, M. J. and Roberts, R. J. and Angeloudi, E. and Huertas-Company, M.},
    title = {{AstroPT: Scaling Large Observation Models for Astronomy}},
    journal = {ArXiv e-prints},
    year = {2024},
    eprint = {2405.14930},
    doi = {10.48550/arXiv.2405.14930}
}