AstroYOLO: A hybrid CNN–Transformer deep-learning object-detection model for blue horizontal-branch stars

作者：Yuchen He, Jingjing Wu, Wenyu Wang, Bin Jiang, Yanxia Zhang · 发表于：Publications of the Astronomical Society of Japan · 年份：2023 · DOI：10.1093/pasj/psad071 · 被引用次数：9 · 研究领域：Stellar, planetary, and galactic studies、CCD and CMOS Imaging Sensors、Gamma-ray bursts and supernovae

Abstract Blue horizontal-branch stars (BHBs) are ideal tracers for studying the Milky Way (MW) due to their bright and nearly constant magnitude. However, an incomplete screen of BHBs from a survey would result in bias of estimation of the structure or mass of the MW. With surveys of large sky telescopes like the Sloan Digital Sky Survey (SDSS), it is possible to obtain a complete sample. Thus, detecting BHBs from massive photometric images quickly and effectually is necessary. The current acquisition methods of BHBs are mainly based on manual or semi-automatic modes. Therefore, novel approaches are required to replace manual or traditional machine-learning detection. The mainstream deep-learning-based object-detection methods are often vanilla convolutional neural networks whose ability to extract global features is limited by the receptive field of the convolution operator. Recently, a new Transformer-based method has benefited from the global receptive field advantage brought by the self-attention mechanism, exceeded the vanilla convolution model in many tasks, and achieved excellent results. Therefore, this paper proposes a hybrid convolution and Transformer model called AstroYOLO to take advantage of the convolution in local feature representation and Transformer’s easier discovery of long-distance feature dependences. We conduct a comparative experiment on the 4799 SDSS DR16 photometric image dataset. The experimental results show that our model achieves 99.25% AP@50, 9...