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Sparse Spatio-Temporal Imaging of Radio Transients

Poster on-line

Published online by Cambridge University Press:  29 August 2019

J. Girard
Affiliation:
AIM/CEA, Université Paris Saclay, France email: julien.girard@cea.fr
M. Jiang
Affiliation:
AIM/CEA, Université Paris Saclay, France email: julien.girard@cea.fr
J-L. Starck
Affiliation:
AIM/CEA, Université Paris Saclay, France email: julien.girard@cea.fr
S. Corbel
Affiliation:
AIM/CEA, Université Paris Saclay, France email: julien.girard@cea.fr
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Abstract

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The next-generation radio telescopes such as LOFAR and SKA will give access to high time-resolution and high instantaneous sensitivity that can be exploited to study slow and fast transients over the whole radio window. The search for radio transients in large datasets also represents a new signal-processing challenge requiring efficient and robust signal reconstruction algorithms. Using sparse representations and the general ‘compressed sensing’ framework, we developed a 2D–1D algorithm based on the primal-dual splitting method. We have performed our sparse 2D–1D reconstruction on three-dimensional data sets containing either simulated or real radio transients, at various levels of SNR and integration times. This report presents a summary of the current level of performance of our method.

Type
Contributed Papers
Copyright
© International Astronomical Union 2019 

Footnotes

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Girard et al. supplementary material

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