Neural Interferometry: Image Reconstruction from Astronomical Interferometers Using Transformer-Conditioned Neural Fields

Authors

  • Benjamin Wu National Astronomical Observatory of Japan NVIDIA
  • Chao Liu NVIDIA
  • Benjamin Eckart NVIDIA
  • Jan Kautz NVIDIA

DOI:

https://doi.org/10.1609/aaai.v36i3.20171

Keywords:

Computer Vision (CV), Domain(s) Of Application (APP)

Abstract

Astronomical interferometry enables a collection of telescopes to achieve angular resolutions comparable to that of a single, much larger telescope. This is achieved by combining simultaneous observations from pairs of telescopes such that the signal is mathematically equivalent to sampling the Fourier domain of the object. However, reconstructing images from such sparse sampling is a challenging and ill-posed problem, with current methods requiring precise tuning of parameters and manual, iterative cleaning by experts. We present a novel deep learning approach in which the representation in the Fourier domain of an astronomical source is learned implicitly using a neural field representation. Data-driven priors can be added through a transformer encoder. Results on synthetically observed galaxies show that transformer-conditioned neural fields can successfully reconstruct astronomical observations even when the number of visibilities is very sparse.

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Published

2022-06-28

How to Cite

Wu, B., Liu, C., Eckart, B., & Kautz, J. (2022). Neural Interferometry: Image Reconstruction from Astronomical Interferometers Using Transformer-Conditioned Neural Fields. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3), 2685-2693. https://doi.org/10.1609/aaai.v36i3.20171

Issue

Section

AAAI Technical Track on Computer Vision III