Bayesian Imaging for Radio Interferometry with Score-Based Priors

• URL: http://arxiv.org/abs/2311.18012v1
• Date: Wed, 29 Nov 2023 19:01:05 GMT
• Title: Bayesian Imaging for Radio Interferometry with Score-Based Priors
• Authors: Noe Dia, M. J. Yantovski-Barth, Alexandre Adam, Micah Bowles, Pablo Lemos, Anna M. M. Scaife, Yashar Hezaveh, Laurence Perreault-Levasseur
• Abstract summary: We show that our method produces plausible posterior samples despite the misspecified galaxy prior. We show that our approach produces results which are competitive with existing radio interferometry imaging algorithms.
• Score: 36.136619420474766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner. We use a score-based prior derived from optical images of galaxies to recover images of protoplanetary disks from the DSHARP survey. We demonstrate that our method produces plausible posterior samples despite the misspecified galaxy prior. We show that our approach produces results which are competitive with existing radio interferometry imaging algorithms.

Related papers

• IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors [36.136619420474766]
  In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS) We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms.
  arXiv  Detail & Related papers  (2025-01-05T08:11:53Z)
• Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions [40.13294159814764]
  Deconvolution of astronomical images is a key aspect of recovering the intrinsic properties of celestial objects. This paper explores the use of diffusion models (DMs) and the Diffusion Posterior Sampling (DPS) algorithm to solve this inverse problem task.
  arXiv  Detail & Related papers  (2024-11-28T14:00:00Z)
• Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry [2.8311497176067104]
  Next-generation radio interferometers like the Square Kilometer Array promise to revolutionise our radio astronomy capabilities. The unprecedented volume of data these devices generate requires fast and accurate image reconstruction algorithms to solve the ill-posed radio interferometric imaging problem. We propose an unsupervised technique based on a conformalized version of a radio-augmented equivariant bootstrapping method, which allows us to quantify uncertainties for fast reconstruction methods.
  arXiv  Detail & Related papers  (2024-10-30T16:36:55Z)
• Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging [5.678038945350452]
  Next-generation radio interferometrics have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensitivity. One key to unlocking their potential resides in handling the deluge and complexity of incoming data. This work proposes a method coined QuantifAI to address uncertainty quantification in radio-interferometric imaging.
  arXiv  Detail & Related papers  (2023-11-30T19:00:02Z)
• Passive superresolution imaging of incoherent objects [63.942632088208505]
  Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions. Deep neural network is used to reconstruct the object from these measurements.
  arXiv  Detail & Related papers  (2023-04-19T15:53:09Z)
• Posterior samples of source galaxies in strong gravitational lenses with score-based priors [107.52670032376555]
  We use a score-based model to encode the prior for the inference of undistorted images of background galaxies. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.
  arXiv  Detail & Related papers  (2022-11-07T19:00:42Z)
• Depth Estimation from Monocular Images and Sparse Radar Data [93.70524512061318]
  In this paper, we explore the possibility of achieving a more accurate depth estimation by fusing monocular images and Radar points using a deep neural network. We find that the noise existing in Radar measurements is one of the main key reasons that prevents one from applying the existing fusion methods. The experiments are conducted on the nuScenes dataset, which is one of the first datasets which features Camera, Radar, and LiDAR recordings in diverse scenes and weather conditions.
  arXiv  Detail & Related papers  (2020-09-30T19:01:33Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.