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DTSTAMP:20220812T074334Z
LOCATION:Foyer 2nd Floor
DTSTART;TZID=Europe/Stockholm:20220628T090000
DTEND;TZID=Europe/Stockholm:20220628T110000
UID:submissions.pasc-conference.org_PASC22_sess181_pos161@linklings.com
SUMMARY:P44 - Image Deconvolution for Next-Generation Radio Interferometry
DESCRIPTION:Poster\n\nP44 - Image Deconvolution for Next-Generation Radio 
 Interferometry\n\nBianco\n\nNext-generation radio interferometers such as 
 the Square Kilometre Array (SKA) will produce massive datasets that will n
 eed to be processed and analysed with efficient imaging techniques. The th
 ousands of serial cleaning iterations required by traditional imaging algo
 rithms cannot scale to the requirements of Big Data radio astronomy. With 
 Bluebild we are developing efficient and user-friendly software for radio 
 astronomy imaging as a modern alternative to the state-of-the-art software
  CLEAN algorithm. Our method employs Principle Component Analysis (PCA) to
  linearly decompose visibilities from interferometric radio telescopes int
 o different energy levels of detected sources in the sky. Bluebild is GPU 
 accelerated, decreasing the time for image processing by several orders of
  magnitude when compared with the contemporary approach. Decomposition of 
 the sky into separate energy levels also allows for a more efficient, para
 llelized application of the deconvolution process. Here, we present possib
 le extensions to Buebild that include deconvolution solutions of the energ
 y levels, and comparison to serial CLEAN deconvolution. We also explore a 
 deep learning method that takes advantage of the sky image linear decompos
 ition of Bluebild to denoise and re-generate a clean image. We show that t
 he deconvolution successfully recovers the signal from the data processor 
 pipeline of SKA precursor telescopes
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