5,358 research outputs found
LOFAR Sparse Image Reconstruction
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital
phased array interferometer with multiple antennas distributed in Europe. It
provides discrete sets of Fourier components of the sky brightness. Recovering
the original brightness distribution with aperture synthesis forms an inverse
problem that can be solved by various deconvolution and minimization methods
Aims. Recent papers have established a clear link between the discrete nature
of radio interferometry measurement and the "compressed sensing" (CS) theory,
which supports sparse reconstruction methods to form an image from the measured
visibilities. Empowered by proximal theory, CS offers a sound framework for
efficient global minimization and sparse data representation using fast
algorithms. Combined with instrumental direction-dependent effects (DDE) in the
scope of a real instrument, we developed and validated a new method based on
this framework Methods. We implemented a sparse reconstruction method in the
standard LOFAR imaging tool and compared the photometric and resolution
performance of this new imager with that of CLEAN-based methods (CLEAN and
MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse
reconstruction performs as well as CLEAN in recovering the flux of point
sources; ii) performs much better on extended objects (the root mean square
error is reduced by a factor of up to 10); and iii) provides a solution with an
effective angular resolution 2-3 times better than the CLEAN images.
Conclusions. Sparse recovery gives a correct photometry on high dynamic and
wide-field images and improved realistic structures of extended sources (of
simulated and real LOFAR datasets). This sparse reconstruction method is
compatible with modern interferometric imagers that handle DDE corrections (A-
and W-projections) required for current and future instruments such as LOFAR
and SKAComment: Published in A&A, 19 pages, 9 figure
Dealing with missing data: An inpainting application to the MICROSCOPE space mission
Missing data are a common problem in experimental and observational physics.
They can be caused by various sources, either an instrument's saturation, or a
contamination from an external event, or a data loss. In particular, they can
have a disastrous effect when one is seeking to characterize a
colored-noise-dominated signal in Fourier space, since they create a spectral
leakage that can artificially increase the noise. It is therefore important to
either take them into account or to correct for them prior to e.g. a
Least-Square fit of the signal to be characterized. In this paper, we present
an application of the {\it inpainting} algorithm to mock MICROSCOPE data; {\it
inpainting} is based on a sparsity assumption, and has already been used in
various astrophysical contexts; MICROSCOPE is a French Space Agency mission,
whose launch is expected in 2016, that aims to test the Weak Equivalence
Principle down to the level. We then explore the {\it inpainting}
dependence on the number of gaps and the total fraction of missing values. We
show that, in a worst-case scenario, after reconstructing missing values with
{\it inpainting}, a Least-Square fit may allow us to significantly measure a
Equivalence Principle violation signal, which is
sufficiently close to the MICROSCOPE requirements to implement {\it inpainting}
in the official MICROSCOPE data processing and analysis pipeline. Together with
the previously published KARMA method, {\it inpainting} will then allow us to
independently characterize and cross-check an Equivalence Principle violation
signal detection down to the level.Comment: Accepted for publication in Physical Review D. 12 pages, 6 figure
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