Super-resolving Herschel - a deep learning based deconvolution and denoising technique
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917153095024640 |
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| author | Koopmans, Dennis Wang, Lingyu Margalef-Bentabol, Berta La Marca, Antonio Bethermin, Matthieu Bisigello, Laura Gao, Zhen-Kai Lagos, Claudia del P. Lauritsen, Lynge Serjeant, Stephen van der Tak, F. F. S. Wang, Wei-Hao |
| author_facet | Koopmans, Dennis Wang, Lingyu Margalef-Bentabol, Berta La Marca, Antonio Bethermin, Matthieu Bisigello, Laura Gao, Zhen-Kai Lagos, Claudia del P. Lauritsen, Lynge Serjeant, Stephen van der Tak, F. F. S. Wang, Wei-Hao |
| contents | 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 $μ$m observations by a factor 4.5, using Spitzer/MIPS 24$μ$m and Herschel/SPIRE 250, 350, 500$μ$m 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 $μ$m observations in the COSMOS field which have superior resolution compared to Herschel. Our SR method achieves an inference time of $1s/deg^2$ 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 $μ$m observations, fluxes of the extracted sources from the super-resolved map agree well with SCUBA-2 measured fluxes for sources $\geq$10 mJy. Our technique enables SR over hundreds of $deg^2$ without the need for fine-tuning, facilitating statistical analysis of DSFGs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Super-resolving Herschel - a deep learning based deconvolution and denoising technique Koopmans, Dennis Wang, Lingyu Margalef-Bentabol, Berta La Marca, Antonio Bethermin, Matthieu Bisigello, Laura Gao, Zhen-Kai Lagos, Claudia del P. Lauritsen, Lynge Serjeant, Stephen van der Tak, F. F. S. Wang, Wei-Hao Astrophysics of Galaxies 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 $μ$m observations by a factor 4.5, using Spitzer/MIPS 24$μ$m and Herschel/SPIRE 250, 350, 500$μ$m 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 $μ$m observations in the COSMOS field which have superior resolution compared to Herschel. Our SR method achieves an inference time of $1s/deg^2$ 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 $μ$m observations, fluxes of the extracted sources from the super-resolved map agree well with SCUBA-2 measured fluxes for sources $\geq$10 mJy. Our technique enables SR over hundreds of $deg^2$ without the need for fine-tuning, facilitating statistical analysis of DSFGs. |
| title | Super-resolving Herschel - a deep learning based deconvolution and denoising technique |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2512.13353 |