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Deep Radio Interferometric Imaging with POLISH: DSA-2000 and weak lensing

Connor, Liam and Bouman, Katherine L. and Ravi, Vikram and Hallinan, Gregg (2021) Deep Radio Interferometric Imaging with POLISH: DSA-2000 and weak lensing. . (Unpublished)

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Radio interferometry allows astronomers to probe small spatial scales that are often inaccessible with single-dish instruments. However, recovering the radio sky from an interferometer is an ill-posed deconvolution problem that astronomers have worked on for half a century. More challenging still is achieving resolution below the array's diffraction limit, known as super-resolution imaging. To this end, we have developed a new learning-based approach for radio interferometric imaging, leveraging recent advances in the computer vision problems deconvolution and single-image super-resolution (SISR). We have developed and trained a high dynamic range residual neural network to learn the mapping between the dirty image and the true radio sky. We call this procedure POLISH, in contrast to the traditional CLEAN algorithm. The feed forward nature of learning-based approaches like POLISH is critical for analyzing data from the upcoming Deep Synoptic Array (DSA-2000). We show that POLISH achieves super-resolution, and we demonstrate its ability to deconvolve real observations from the Very Large Array (VLA). Super-resolution on DSA-2000 will allow us to measure the shapes and orientations of several hundred million star forming radio galaxies (SFGs), making it a powerful cosmological weak lensing survey and probe of dark energy. We forecast its ability to constrain the lensing power spectrum, finding that it will be complementary to next-generation optical surveys such as Euclid.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Connor, Liam0000-0002-7587-6352
Bouman, Katherine L.0000-0003-0077-4367
Ravi, Vikram0000-0002-7252-5485
Hallinan, Gregg0000-0002-7083-4049
Additional Information:Attribution 4.0 International (CC BY 4.0). We are grateful to Schmidt Futures for supporting the Radio Camera Initiative, under which this work was carried out. We thank Martin Krasser for his Tensorflow 2.x implementation of several super-resolution algorithms and his advice on this paper's application. We thank Joe Zuntz for help with his CosmoSIS code and Ian Harrison for valuable conversations. We also thank Nitika Yadlapalli, Yuping Huang, and Jamie Bock for helpful discussion.
Group:Astronomy Department
Funding AgencyGrant Number
Schmidt Futures ProgramUNSPECIFIED
Subject Keywords:methods, machine learning
Record Number:CaltechAUTHORS:20220125-215138493
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113105
Deposited By: George Porter
Deposited On:26 Jan 2022 15:16
Last Modified:26 Jan 2022 15:16

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