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Strong-Lensing Source Reconstruction with Denoising Diffusion Restoration Models

K Karchev, NA Montel, A Coogan… - arXiv preprint arXiv …, 2022 - arxiv.org
Astrophysics paper astro-ph.IM Suggest

… We expect the denoising diffusion approach to source reconstruction to prove instrumental in generating constrained training examples for simulation-based inference of dark matter …

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BibTeX

@article{2211.04365v1,
Author        = {Konstantin Karchev and Noemi Anau Montel and Adam Coogan and Christoph Weniger},
Title         = {Strong-Lensing Source Reconstruction with Denoising Diffusion Restoration Models},
Eprint        = {2211.04365v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Analysis of galaxy--galaxy strong lensing systems is strongly dependent on any prior assumptions made about the appearance of the source. Here we present a method of imposing a data-driven prior / regularisation for source galaxies based on denoising diffusion probabilistic models (DDPMs). We use a pre-trained model for galaxy images, AstroDDPM, and a chain of conditional reconstruction steps called denoising diffusion reconstruction model (DDRM) to obtain samples consistent both with the noisy observation and with the distribution of training data for AstroDDPM. We show that these samples have the qualitative properties associated with the posterior for the source model: in a low-to-medium noise scenario they closely resemble the observation, while reconstructions from uncertain data show greater variability, consistent with the distribution encoded in the generative model used as prior.},
Year          = {2022},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2211.04365v1},
File          = {2211.04365v1.pdf}
}

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