# Deep Learning Segmentation of Spiral Arms and Bars

arXiv: https://arxiv.org/abs/2312.02908 · HTML: https://mlsanity.com/paper/2312.02908/

## 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 (most cited first, 7 of 7)

- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies](https://mlsanity.com/paper/2102.08414/) (2021)
- [Realistic galaxy image simulation via score-based generative models](https://mlsanity.com/paper/2111.01713/) (2021)
- [Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning](https://mlsanity.com/paper/1905.07424/) (2019)
- [Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data](https://mlsanity.com/paper/1906.11248/) (2019)
- [Towards Galaxy Foundation Models with Hybrid Contrastive Learning](https://mlsanity.com/paper/2206.11927/) (2022)
- [Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation](https://mlsanity.com/paper/2009.06639/) (2020)

## 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.