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arXiv 2103.08127astro-ph.GAastro-ph.IM
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用深度学习量化盘状星系的精细结构:不同哈勃类型中螺旋臂的分割

Quantifying the fine structures of disk galaxies with deep learning:Segmentation of spiral arms in different Hubble types

Kenji Bekki

摘要:

螺旋臂和其他星系成分(如巨型分子云和大质量OB星)之间的空间相关性表明,螺旋臂在盘状星系演化的各个方面都发挥着至关重要的作用。因此,盘星系旋臂的分割是研究这些相关性的一个关键任务。我们在这里尝试使用U-网将盘状星系分解为螺旋和非螺旋区域,U-网基于深度学习算法,并已被发明用于生物学中的分割任务。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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