Fourier dimensionality reduction for fast radio transients

Ming Jiang, Vijay Kartik, Jean-Philippe Thiran, Yves Wiaux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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In the context of next-generation radio interferometers, we
are facing a big challenge of how to economically process data. The
classical averaging method to reduce the data is not optimal and can
even produce false negative errors in some cases, such as fast radio
transients (FRT) imaging, where temporal resolution is required. We
propose a robust dimensionality reduction method for FRT imaging in
the framework of compressed sensing-based imaging algorithms. For each
time slice of FRT imaging, our dimensionality reduction defines a linear
embedding operator to reduce the space spanned by the left singular
vectors of the measurement operator, which can be considered as a fast
approximation of singular value decomposition (SVD). The preliminary
results on simulated FRT showcase that the proposed dimensionality
reduction can simultaneously reduce the data significantly and recover
FRT correctly, while the averaging technique causes the FRT dilution
Original languageEnglish
Title of host publicationProceedings of the International BASP Frontiers Workshop 2019
Number of pages1
Publication statusPublished - 2019
EventInternational BASP Frontiers workshop 2019 - Villars sur Ollon, Switzerland
Duration: 3 Feb 20198 Feb 2019


WorkshopInternational BASP Frontiers workshop 2019
CityVillars sur Ollon
Internet address


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