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.

我们提出了第一个用于分割银河螺旋臂和棒状结构的深度学习模型。经过专家天文学家的盲评估，我们预测的螺旋臂掩模优于当前的自动化方法（99% 的评估）和我们的原始志愿者标签（79% 的评估）。专家评价我们的螺旋臂掩模在 89% 的评估中被评为 “大部分良好” 至 “完美”。从我们预测的棒状结构掩模派生出的棒长与一个专门的众包项目完全一致。我们掩模的像素级精度在以前是不可能的，在新的研究中将支撑螺旋臂和棒的演化。