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dc.contributor.authorWalmsley, Mike
dc.contributor.authorSpindler, Ashley
dc.date.accessioned2024-03-25T13:03:07Z
dc.date.available2024-03-25T13:03:07Z
dc.date.issued2023-12
dc.identifier.citationWalmsley , 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.citationworkshop
dc.identifier.otherORCID: /0000-0003-0198-3881/work/152841873
dc.identifier.urihttp://hdl.handle.net/2299/27490
dc.description© 2023 Machine Learning and the Physical Sciences Workshop, NeurIPS.
dc.description.abstractWe 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.extent10
dc.format.extent759568
dc.language.isoeng
dc.relation.ispartof
dc.titleDeep Learning Segmentation of Spiral Arms and Barsen
dc.contributor.institutionCentre for Astrophysics Research (CAR)
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Physics, Astronomy and Mathematics
dc.description.statusPeer reviewed
dc.identifier.urlhttps://ml4physicalsciences.github.io/2023/
dc.identifier.urlhttps://doi.org/10.48550/arXiv.2312.02908
dc.identifier.urlhttps://ml4physicalsciences.github.io/2023/files/NeurIPS_ML4PS_2023_190.pdf
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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