arXiv:2103.08127 [astro-ph.GA]AbstractReferencesReviewsResources
Quantifying the fine structures of disk galaxies with deep learning:Segmentation of spiral arms in different Hubble types
Published 2021-03-15Version 1
Spatial correlations between spiral arms and other galactic components such as giant molecular clouds and massive OB stars suggest that spiral arms can play vital roles in various aspects of disk galaxy evolution. Segmentation of spiral arms in disk galaxies is therefore a key task to investigate these correlations. We here try to decompose disk galaxies into spiral and non-spiral regions by using U-net, which is based on deep learning algorithms and has been invented for segmentation tasks in biology.
Comments: Accepted by A&A (15 pages, 15 figures)
Categories: astro-ph.GA, astro-ph.IM
Related articles: Most relevant | Search more
arXiv:2103.01072 [astro-ph.GA] (Published 2021-03-01)
Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning
arXiv:1407.0509 [astro-ph.GA] (Published 2014-07-02)
Fractal basins of escape and the formation of spiral arms in a galactic potential with a bar
arXiv:1501.07649 [astro-ph.GA] (Published 2015-01-30)
The Contribution of Spiral Arms to the Thick Disk along the Hubble Sequence