2025/12/15 by Koopmans, Dennis, Wang, Lingyu, Margalef-Bentabol, Berta +9
#Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences
paper · doi:10.48550/arxiv.2512.13353
Dusty star-forming galaxies (DSFGs) dominate the far-infrared and sub-millimetre number counts, but single-dish surveys suffer from poor angular resolution, complicating mult-wavelength counterpart identification. Prior-driven deblending techniques require extensive fine-tuning and struggle to process large fields. This work aims to develop a fast, reliable deep-learning based deconvolution and denoising super-resolution (SR) technique. We employ a transformer neural network to improve the resolution of Herschel/SPIRE 500 μm observations by a factor 4.5, using Spitzer/MIPS 24μm and Herschel/SPIRE 250, 350, 500μm images. Trained on SIDES and SHARK simulations, we injected instrumental noise into the input simulated images, while keeping the target images noise-free to enhance de-noising capabilities of our method. We evaluated the performance on simulated test sets and real JCMT/SCUBA-2 450 μm observations in the COSMOS field which have superior resolution compared to Herschel. Our SR method achieves an inference time of 1s/deg2 on consumer GPUs, much faster than traditional deblending techniques. Using the simulation test sets, we show that fluxes of the extracted sources from the super-resolved image are accurate to within 5% for sources with an intrinsic flux \gtrsim 8 mJy, which is a substantial improvement compared to blind extraction on the native images. Astrometric error is low (\lesssim 1" vs 12" pixel scale). Reliability is \gtrsim 90% for sources >3 mJy and >90% of sources with intrinsic fluxes \gtrsim5 mJy are recovered. Applied to real 500 μm observations, fluxes of the extracted sources from the super-resolved map agree well with SCUBA-2 measured fluxes for sources ≥10 mJy. Our technique enables SR over hundreds of deg2 without the need for fine-tuning, facilitating statistical analysis of DSFGs.