Metraipolis › archive › dc_b378bc668d2f

Generating radio continuum survey maps of arbitrary size with latent diffusion models — arXiv:2609.06549

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.06549
License (as archived): CC0-1.0
Archived: 2026-10-16T16:06:52.136Z
SHA-256 of the archived text: 14c534e7fa7d3f4afeefe88701083d9fd28763cb9661d354d60b91a0d38e557a
Stable URL of this page: https://metraipolis.com/documento/dc_b378bc668d2f

How to cite

Generating radio continuum survey maps of arbitrary size with latent diffusion models — arXiv:2609.06549 — arXiv (en), https://arxiv.org/abs/2609.06549 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b378bc668d2f (Metraipolis archive dc_b378bc668d2f, sha256 14c534e7fa7d3f4afeefe88701083d9fd28763cb9661d354d60b91a0d38e557a)

BibTeX

@misc{dcb378bc668d2f,
  title = {Generating radio continuum survey maps of arbitrary size with latent diffusion models — arXiv:2609.06549},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.06549},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b378bc668d2f; sha256 14c534e7fa7d3f4afeefe88701083d9fd28763cb9661d354d60b91a0d38e557a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.06549. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Radio surveys provide exponentially increasing amounts of observational data, which requires the development of novel data reduction and analysis methods. To this end, realistic simulations of observational data provide controlled environments for testing and developing new methods. Diffusion models excel at synthesizing realistic image data, but are limited by size constraints and high computational demands. We implemented a latent diffusion model (LDM) to synthesize realistic radio survey map cutouts. Further, we developed a technique for sampling radio survey maps of arbitrary size with the LDM. This is done by sampling in multiple sequential steps, whereby consistency is provided through context vectors and image inpainting. We trained our model on observations from the LOFAR telescope obtained from the third data release of the LOFAR Two-Metre Sky Survey. We implemented a LDM composed of a vector-quantized variational autoencoder and a diffusion model operating on the resulting latent representations. We added information on source locations, brightness and sizes by extracting parameters from the public source catalog and encoding them into a vector format passed as context to the LDM. We trained the model for image inpainting to facilitate continuous sequential sampling. We were able to generate realistic cutouts of 512 pixel side length, corresponding to ~5.7 arcmin, with precise control over source locations and brightness. Control over source extension was achieved at lower precision. Sampling of arbitrary-sized maps was made possible, showing realistic results with seamless combination of individual samples and limited only by occasional sampling artifacts.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-09 in astro-ph.IM).
Categorie arXiv: Instrumentation and Methods for Astrophysics (astro-ph.IM).
Pagina del lavoro: https://arxiv.org/abs/2609.06549 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T15:48:26.417Z dalla pagina https://arxiv.org/catchup/astro-ph.IM/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).