كيف تعثر شبكة Faster R-CNN العصبية على تجمعات نجمية عملاقة في مئات الآلاف من المجرات ✛ ✦

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2607.04176

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.