2,585 research outputs found
Compression of interferometric radio-astronomical data
The volume of radio-astronomical data is a considerable burden in the
processing and storing of radio observations with high time and frequency
resolutions and large bandwidths. Lossy compression of interferometric
radio-astronomical data is considered to reduce the volume of visibility data
and to speed up processing.
A new compression technique named "Dysco" is introduced that consists of two
steps: a normalization step, in which grouped visibilities are normalized to
have a similar distribution; and a quantization and encoding step, which rounds
values to a given quantization scheme using a dithering scheme. Several
non-linear quantization schemes are tested and combined with different methods
for normalizing the data. Four data sets with observations from the LOFAR and
MWA telescopes are processed with different processing strategies and different
combinations of normalization and quantization. The effects of compression are
measured in image plane.
The noise added by the lossy compression technique acts like normal system
noise. The accuracy of Dysco is depending on the signal-to-noise ratio of the
data: noisy data can be compressed with a smaller loss of image quality. Data
with typical correlator time and frequency resolutions can be compressed by a
factor of 6.4 for LOFAR and 5.3 for MWA observations with less than 1% added
system noise. An implementation of the compression technique is released that
provides a Casacore storage manager and allows transparent encoding and
decoding. Encoding and decoding is faster than the read/write speed of typical
disks.
The technique can be used for LOFAR and MWA to reduce the archival space
requirements for storing observed data. Data from SKA-low will likely be
compressible by the same amount as LOFAR. The same technique can be used to
compress data from other telescopes, but a different bit-rate might be
required.Comment: Accepted for publication in A&A. 13 pages, 8 figures. Abstract was
abridge
Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
As the world's largest radio telescope, the Square Kilometer Array (SKA) will
provide radio interferometric data with unprecedented detail. Image
reconstruction algorithms for radio interferometry are challenged to scale well
with TeraByte image sizes never seen before. In this work, we investigate one
such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image
reconstruction For radio INterferometry). In particular, we focus on the
challenging task of automatically finding the optimal regularization parameter
values. In practice, finding the regularization parameters using classical grid
search is computationally intensive and nontrivial due to the lack of ground-
truth. We adopt a greedy strategy where, at each iteration, the optimal
parameters are found by minimizing the predicted Stein unbiased risk estimate
(PSURE). The proposed self-tuned version of MUFFIN involves parallel and
computationally efficient steps, and scales well with large- scale data.
Finally, numerical results on a 3D image are presented to showcase the
performance of the proposed approach
A morphological algorithm for improving radio-frequency interference detection
A technique is described that is used to improve the detection of
radio-frequency interference in astronomical radio observatories. It is applied
on a two-dimensional interference mask after regular detection in the
time-frequency domain with existing techniques. The scale-invariant rank (SIR)
operator is defined, which is a one-dimensional mathematical morphology
technique that can be used to find adjacent intervals in the time or frequency
domain that are likely to be affected by RFI. The technique might also be
applicable in other areas in which morphological scale-invariant behaviour is
desired, such as source detection. A new algorithm is described, that is shown
to perform quite well, has linear time complexity and is fast enough to be
applied in modern high resolution observatories. It is used in the default
pipeline of the LOFAR observatory.Comment: Accepted for publication in A&
Supervised Neural Networks for RFI Flagging
Neural network (NN) based methods are applied to the detection of radio
frequency interference (RFI) in post-correlation,post-calibration
time/frequency data. While calibration doesaffect RFI for the sake of this work
a reduced dataset inpost-calibration is used. Two machine learning
approachesfor flagging real measurement data are demonstrated usingthe existing
RFI flagging technique AOFlagger as a groundtruth. It is shown that a single
layer fully connects networkcan be trained using each time/frequency sample
individuallywith the magnitude and phase of each polarization and
Stokesvisibilities as features. This method was able to predict aBoolean flag
map for each baseline to a high degree of accuracy achieving a Recall of 0.69
and Precision of 0.83 and anF1-Score of 0.75.Comment: This paper has been published in the Proceedings of RFI 2019 Workshop
by IEEE Xplorer at:
https://ieeexplore.ieee.org/xpl/conhome/9108774/proceedin
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