Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP

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 ($z\lesssim 0.5$) 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 $\text{textsc}Zoobot$ foundation DL-model as a feature extraction backbone, we also demonstrate one of the first applications of $\text{textsc}Zoobot$ 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.