dc.contributor.author | Walmsley, Mike | |
dc.contributor.author | Spindler, Ashley | |
dc.date.accessioned | 2024-03-25T13:03:07Z | |
dc.date.available | 2024-03-25T13:03:07Z | |
dc.date.issued | 2023-12 | |
dc.identifier.citation | Walmsley , M & Spindler , A 2023 , ' Deep Learning Segmentation of Spiral Arms and Bars ' , Paper presented at Machine Learning and the Physical Sciences Workshop at NuerIPS 2023 , New Orleans , United States , 15/12/23 - 15/12/23 pp. 1-10 . < https://ml4physicalsciences.github.io/2023/files/NeurIPS_ML4PS_2023_190.pdf > | |
dc.identifier.citation | workshop | |
dc.identifier.other | ORCID: /0000-0003-0198-3881/work/152841873 | |
dc.identifier.uri | http://hdl.handle.net/2299/27490 | |
dc.description | © 2023 Machine Learning and the Physical Sciences Workshop, NeurIPS. | |
dc.description.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. | en |
dc.format.extent | 10 | |
dc.format.extent | 759568 | |
dc.language.iso | eng | |
dc.relation.ispartof | | |
dc.title | Deep Learning Segmentation of Spiral Arms and Bars | en |
dc.contributor.institution | Centre for Astrophysics Research (CAR) | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Department of Physics, Astronomy and Mathematics | |
dc.description.status | Peer reviewed | |
dc.identifier.url | https://ml4physicalsciences.github.io/2023/ | |
dc.identifier.url | https://doi.org/10.48550/arXiv.2312.02908 | |
dc.identifier.url | https://ml4physicalsciences.github.io/2023/files/NeurIPS_ML4PS_2023_190.pdf | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |