AI 中文总结
利用深度学习的目标检测模型，通过Faster R - CNN处理$ugrizy$波段图像，检测低红移星系中潜在恒星形成团块，验证了模型的检测完整性和纯度，还展示了Zoobot在目标检测下游任务中的应用。
AI 中文摘要
巨型恒星形成团块（GSFCs）是恒星形成增强的千秒差距尺度区域，在高红移星系中常见，低红移星系中罕见。利用深度学习目标检测模型，通过处理HSC - SSP和CLAUDS的$ugrizy$图像检测低红移星系团块，基于Faster R - CNN扩展模型，采用Zoobot提取特征，验证了模型的检测完整性和纯度。
英文摘要
Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of $10^7$ to $10^9\,M_\odot$ that are commonly observed in high-redshift galaxies but are rarely detected in low-redshift ($z\lesssim0.5$) galaxy analogues. However, the availability of wide-field galaxy survey data makes it possible to identify potential star-forming clumps in large samples of low-redshift galaxies using object detection models that are based on Deep Learning (DL) techniques. We apply a novel DL-based object detection model to galaxies observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). Our model is based on the the Faster Region-Based Convolutional Neural Network (Faster R-CNN or FRCNN) object detection framework but expanded to process the six $ugrizy$ filter band images simultaneously and identify not only clumps and their locations in the host galaxy but also additional contaminants. By adopting the \textsc{Zoobot} foundation DL-model as a feature extraction backbone, we also demonstrate one of the first applications of \textsc{Zoobot} in a downstream task for object detection. Our model achieves a detection completeness of $\gtrsim 0.9$ and purity of $\gtrsim 0.8$ which were validated on a large set of real galaxies into which simulated clumps were injected.
CommentsAccepted 2026 July 14 by RASTI
