mlsanityData to 7 Oct 2026

Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies

arXiv 2102.08414 ↗First posted 16 Feb 2021astro-ph.GA cs.CVcited by 40

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Abstract (arXiv)

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r=23.6 vs. r=22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314,000 galaxies. 140,000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314,000 galaxies. When measured against confident volunteer classifications, the networks are approximately 99% accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 2 of its 29 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

2 of 29 references.

References · 2

  1. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  2. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 10; 2024: 6; 2025: 15; 2026: 5.

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  1. 2023MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splittingcited by 33
  2. 2024A Generative Model of Symmetry Transformationscited by 10
  3. 2023The Representation Jensen-Shannon Divergencecited by 9
  4. 2023DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detectioncited by 8
  5. 2023Astroformer: More Data Might not be all you need for Classificationcited by 6
  6. 2023E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classificationcited by 6
  7. 2025AstroLLaVA: towards the unification of astronomical data and natural languagecited by 6
  8. 2024Scaling Laws for Galaxy Imagescited by 5
  9. 2022Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detectioncited by 4
  10. 2023MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learningcited by 4
  11. 2024Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decompositioncited by 4
  12. 2024GalaxAlign: Mimicking Citizen Scientists' Multimodal Guidance for Galaxy Morphology Analysiscited by 4
28 more
  1. 2025Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understandingcited by 4
  2. 2025Active Measurement: Efficient Estimation at Scalecited by 4
  3. 2024Multi LoRA Meets Vision: Merging multiple adapters to create a multi task modelcited by 3
  4. 2021A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classificationcited by 2
  5. 2022The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniquescited by 2
  6. 2022Elements of effective machine learning datasets in astronomycited by 2
  7. 2023Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priorscited by 2
  8. 2024Evaluating Bayesian deep learning for radio galaxy classificationcited by 2
  9. 2025SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networkscited by 2
  10. 2025Are vision language models robust to uncertain inputs?cited by 2
  11. 2025DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretrainingcited by 2
  12. 2023Morphological Classification of Galaxies Using SpinalNetcited by 1
  13. 2025AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learningcited by 1
  14. 2025Category-based Galaxy Image Generation via Diffusion Modelscited by 1
  15. 2025Galaxy image simplification using Generative AIcited by 1
  16. 2025Semantic search for 100M+ galaxy images using AI-generated captionscited by 1
  17. 2026OmniScientist: An Omni-Modal Omni-Discipline AI Scientistcited by 1
  18. 2023Spiral-Elliptical automated galaxy morphology classification from telescope imagescited by 0
  19. 2023Deep Learning Segmentation of Spiral Arms and Barscited by 0
  20. 2025Kernel Quantile Embeddings and Associated Probability Metricscited by 0
  21. 2025Overlap-aware segmentation for topological reconstruction of obscured objectscited by 0
  22. 2025No-Rank Tensor Decomposition Using Metric Learningcited by 0
  23. 2025Equivariant Sparse Autoencoders: Mechanistic Interpretability of Neural Networks on Symmetric Datacited by 0
  24. 2025Compact neural networks for astronomy with optimal transport bias correctioncited by 0
  25. 2026Low-Frequency Shortcuts in Texture-Driven Visual Learningcited by 0
  26. 2026Multiclass Classification without Labels via Posterior Simplex Geometrycited by 0
  27. 2026A survey detection channel overrides the pixels in an astronomical foundation model, and biases tomographic mean redshiftscited by 0
  28. 2026Gestalt: a meta-foundation model for astronomycited by 0