The VLA Frontier Field Survey: A Comparison of the Radio and UV/Optical Size of 0.3 ≲ z ≲ 3 Star-forming Galaxies

Jiménez-Andrade, E. F.; Murphy, E. J.; Heywood, Ian; Smail, Ian; Penner, K.; Momjian, Emmanuel; Dickinson, Mark; Armus, L.; Lazio, T. Joseph W.

doi:10.3847/1538-4357/abe876

Cited by 14 publications

(2 citation statements)

References 134 publications

(249 reference statements)

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“…Owen & Morrison ( 2008 ) find that half of SFGs abo v e 15 μJy hav e major axes greater than 1 arcsec. Ho we ver, recent work has argued that μJy radio sources may be more compact than previously believed, with a median FWHM of 0.3 arcsec (Cotton et al 2018 ;Jim énez-Andrade et al 2021 ).…”

Section: Forecastingmentioning

confidence: 46%

Deep radio-interferometric imaging with POLISH: DSA-2000 and weak lensing

Connor

Bouman

Ravi

et al. 2022

Monthly Notices of the Royal Astronomical Society

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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 classical computer vision problems of single-image super-resolution (SISR) and deconvolution. 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.

show abstract

Section: Forecastingmentioning

confidence: 46%

Deep radio-interferometric imaging with POLISH: DSA-2000 and weak lensing

Connor

Bouman

Ravi

et al. 2022

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

show abstract

“…Owen & Morrison (2008) find that half of SFGs above 15 µJy have major-axes greater than 1". However, recent work has argued that µJy radio sources may be more compact than previously believed, with a median FWHM of 0.3" (Cotton et al 2018;Jiménez-Andrade et al 2021). For this reason, we assume that only one half of sources will be resolved with POLISH, assuming an effective resolution of 3"/SNR.…”

Section: Forecastingmentioning

confidence: 99%

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

Connor,

Bouman,

Ravi

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract