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

arXiv:2607.04176 · astro-ph.IM, astro-ph.GA · Submitted 2026-07-05 · Read on arXiv

Jürgen J. Popp, Hugh Dickinson, Stephen Serjeant, Lucy F. Fortson, Tobias Géron, Brooke D. Simmons, Vihang Mehta

astro-ph.IM, astro-ph.GA

Submitted: 2026-07-05

Comments: Accepted 2026 July 14 by RASTI

Code: https://github.com/ou-astrophysics/Zoobot-for-image-segmentation-and-object-detection

License: http://creativecommons.org/licenses/by/4.0/

The gist: Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of 10 7 to 10 9,M that are commonly observed in high-redshift galaxies but are rarely detected

Terminology

Fine-Tuning — 22× in this paper · explained in 5 episodes
Transfer Learning — 16× in this paper · explained in 10 episodes
Feature Extraction — 13× in this paper · explained in 2 episodes
Adam — 9× in this paper · explained in 2 episodes
Aperture — 9× in this paper · explained in 1 episode
Deep Learning — 5× in this paper
Foundation Model — 4× in this paper · explained in 3 episodes
Consistency — 3× in this paper · explained in 3 episodes
Dust Attenuation — 3× in this paper · explained in 2 episodes
Metallicity — 3× in this paper · explained in 3 episodes
Aperture Photometry — 2× in this paper · explained in 1 episode
CFHT Large Area U-band Deep Survey — 2× in this paper
Downstream Tasks — 2× in this paper · explained in 2 episodes
Aperture Correction — 1× in this paper · explained in 1 episode
Conference on Computer Vision and Pattern Recognition — 1× in this paper
Core — 1× in this paper · explained in 2 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
IEEE International Conference on Computer Vision — 1× in this paper
Population Synthesis — 1× in this paper · explained in 2 episodes
Region Proposal Network — 1× in this paper
Segmentation Masks — 1× in this paper · explained in 2 episodes

Abstract

Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of 10 7 to 10 9,M that are commonly observed in high-redshift galaxies but are rarely detected in low-redshift (z 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 Zoobot foundation DL-model as a feature extraction backbone, we also demonstrate one of the first applications of Zoobot in a downstream task for object detection. Our model achieves a detection completeness of 0.9 and purity of 0.8 which were validated on a large set of real galaxies into which simulated clumps were injected.

Sources

SSD: Single Shot MultiBox Detector

Terminology

Abstract

Sources

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