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

arXiv: https://arxiv.org/abs/2102.08414 · HTML: https://mlsanity.com/paper/2102.08414/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)

## Built on by (most cited first, 30 of 41)

- [MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting](https://mlsanity.com/paper/2306.08777/) (2023)
- [A Generative Model of Symmetry Transformations](https://mlsanity.com/paper/2403.01946/) (2024)
- [The Representation Jensen-Shannon Divergence](https://mlsanity.com/paper/2305.16446/) (2023)
- [DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection](https://mlsanity.com/paper/2302.02005/) (2023)
- [E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification](https://mlsanity.com/paper/2311.01500/) (2023)
- [Astroformer: More Data Might not be all you need for Classification](https://mlsanity.com/paper/2304.05350/) (2023)
- [AstroLLaVA: towards the unification of astronomical data and natural language](https://mlsanity.com/paper/2504.08583/) (2025)
- [Scaling Laws for Galaxy Images](https://mlsanity.com/paper/2404.02973/) (2024)
- [Active Measurement: Efficient Estimation at Scale](https://mlsanity.com/paper/2507.01372/) (2025)
- [Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection](https://mlsanity.com/paper/2211.00677/) (2022)
- [MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning](https://mlsanity.com/paper/2305.11108/) (2023)
- [Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition](https://mlsanity.com/paper/2410.02069/) (2024)
- [GalaxAlign: Mimicking Citizen Scientists' Multimodal Guidance for Galaxy Morphology Analysis](https://mlsanity.com/paper/2411.19475/) (2024)
- [Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding](https://mlsanity.com/paper/2503.18578/) (2025)
- [Multi LoRA Meets Vision: Merging multiple adapters to create a multi task model](https://mlsanity.com/paper/2411.14064/) (2024)
- [Are vision language models robust to uncertain inputs?](https://mlsanity.com/paper/2505.11804/) (2025)
- [DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining](https://mlsanity.com/paper/2509.06990/) (2025)
- [A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification](https://mlsanity.com/paper/2111.04353/) (2021)
- [The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques](https://mlsanity.com/paper/2206.06165/) (2022)
- [Elements of effective machine learning datasets in astronomy](https://mlsanity.com/paper/2211.14401/) (2022)
- [Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors](https://mlsanity.com/paper/2311.18002/) (2023)
- [Evaluating Bayesian deep learning for radio galaxy classification](https://mlsanity.com/paper/2405.18351/) (2024)
- [SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks](https://mlsanity.com/paper/2501.14048/) (2025)
- [Morphological Classification of Galaxies Using SpinalNet](https://mlsanity.com/paper/2305.01873/) (2023)
- [AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning](https://mlsanity.com/paper/2505.03509/) (2025)
- [Category-based Galaxy Image Generation via Diffusion Models](https://mlsanity.com/paper/2506.16255/) (2025)
- [Galaxy image simplification using Generative AI](https://mlsanity.com/paper/2507.11692/) (2025)
- [Semantic search for 100M+ galaxy images using AI-generated captions](https://mlsanity.com/paper/2512.11982/) (2025)
- [OmniScientist: An Omni-Modal Omni-Discipline AI Scientist](https://mlsanity.com/paper/2608.13558/) (2026)
- [Spiral-Elliptical automated galaxy morphology classification from telescope images](https://mlsanity.com/paper/2310.07740/) (2023)

## 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.