Deep learning-based deconvolution for interferometric radio transient
  reconstruction
Benjamin Naoto Chiche Julien N. Girard Joana Frontera-Pons Arnaud Woiselle Jean-Luc Starck
Radio astronomy is currently thriving with new large ground-based radio
telescopes coming online in preparation for the upcoming Square Kilometre Array
(SKA). Facilities like LOFAR, MeerKAT/SKA, ASKAP/SKA, and the future SKA-LOW
bring tremendous sensitivity in time and frequency, improved angular
resolution, and also high-rate data streams that need to be processed. They
enable advanced studies of radio transients, volatile by nature, that can be
detected or missed in the data. These transients are markers of high-energy
accelerations of electrons and manifest in a wide range of temporal scales.
Usually studied with dynamic spectroscopy of time series analysis, there is a
motivation to search for such sources in large interferometric datasets. This
requires efficient and robust signal reconstruction algorithms. To correctly
account for the temporal dependency of the data, we improve the classical image
deconvolution inverse problem by adding the temporal dependency in the
reconstruction problem. Then, we introduce two novel neural network
architectures that can do both spatial and temporal modeling of the data and
the instrumental response. Then, we simulate representative time-dependent
image cubes of point source distributions and realistic telescope pointings of
MeerKAT to generate toy models to build the training, validation, and test
datasets. Finally, based on the test data, we evaluate the source profile
reconstruction performance of the proposed methods and classical image
deconvolution algorithm CLEAN applied frame-by-frame. In the presence of
increasing noise level in data frame, the proposed methods display a high level
of robustness compared to frame-by-frame imaging with CLEAN. The deconvolved
image cubes bring a factor of 3 improvement in fidelity of the recovered
temporal profiles and a factor of 2 improvement in background denoising.