Deep Learning Segmentation of Spiral Arms and Bars
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Abstract (arXiv)
We present the first deep learning model for segmenting galactic spiral arms and bars. In a blinded assessment by expert astronomers, our predicted spiral arm masks are preferred over both current automated methods (99% of evaluations) and our original volunteer labels (79% of evaluations). Experts rated our spiral arm masks as `mostly good' to `perfect' in 89% of evaluations. Bar lengths trivially derived from our predicted bar masks are in excellent agreement with a dedicated crowdsourcing project. The pixelwise precision of our masks, previously impossible at scale, will underpin new research into how spiral arms and bars evolve.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 39 references that are in the map. First posted on arXiv 5 Dec 2023.
Builds on
7 of 39 references.
References · 7
- 2015Rotation-invariant convolutional neural networks for galaxy morphology predictioncited by 83
- 2021Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxiescited by 41
- 2021Realistic galaxy image simulation via score-based generative modelscited by 24
- 2019Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learningcited by 22
- 2019Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Datacited by 15
- 2022Towards Galaxy Foundation Models with Hybrid Contrastive Learningcited by 10
- 2020Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentationcited by 4
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