Star-forming clump detection in nearby galaxies using Faster R-CNN and imaging data from CLAUDS and HSC-SSP
arXiv:2607.04176 · doi:10.1093/rasti/rzag051
The paper presents a deep‑learning object detection pipeline based on Faster R‑CNN and a Zoobot backbone to locate giant star‑forming clumps in low‑redshift galaxies using six‑band imaging from CLAUDS and HSC‑SSP, achieving high completeness and purity.
Abstract
Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of to that are commonly observed in high-redshift galaxies but are rarely detected in low-redshift () 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 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 and purity of which were validated on a large set of real galaxies into which simulated clumps were injected.
Accepted 2026 July 14 by RASTI