Abstract
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.
| Original language | English |
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| Pages | 1-10 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - Dec 2023 |
| Event | Machine Learning and the Physical Sciences Workshop at NuerIPS 2023: Workshop at the 37th conference on Neural Information Processing Systems (NeurIPS) - New Orleans Ernest N. Morial Convention Center, New Orleans, United States Duration: 15 Dec 2023 → 15 Dec 2023 Conference number: 37 https://ml4physicalsciences.github.io/2023/ |
Workshop
| Workshop | Machine Learning and the Physical Sciences Workshop at NuerIPS 2023 |
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| Country/Territory | United States |
| City | New Orleans |
| Period | 15/12/23 → 15/12/23 |
| Other | The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive and leading-edge venue for research and discussions at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (ML for physics), (2) developments in ML motivated by physical insights (physics for ML), and most recently (3) convergence of ML and physical sciences (physics with ML) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the futur |
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