Calibration Challenges for Future Radio Telescopes

Instruments for radio astronomical observations have come a long way. While the first telescopes were based on very large dishes and 2-antenna interferometers, current instruments consist of dozens of steerable dishes, whereas future instruments will be even larger distributed sensor arrays with a hierarchy of phased array elements. For such arrays to provide meaningful output (images), accurate calibration is of critical importance. Calibration must solve for the unknown antenna gains and phases, as well as the unknown atmospheric and ionospheric disturbances. Future telescopes will have a large number of elements and a large field of view. In this case the parameters are strongly direction dependent, resulting in a large number of unknown parameters even if appropriately constrained physical or phenomenological descriptions are used. This makes calibration a daunting parameter estimation task, that is reviewed from a signal processing perspective in this article.Comment: 12 pages, 7 figures, 20 subfigures The title quoted in the meta-data is the title after release / final editing

Progress with the LOFAR Imaging Pipeline

One of the science drivers of the new Low Frequency Array (LOFAR) is large-area surveys of the low-frequency radio sky. Realizing this goal requires automated processing of the interferometric data, such that fully calibrated images are produced by the system during survey operations. The LOFAR Imaging Pipeline is the tool intended for this purpose, and is now undergoing significant commissioning work. The pipeline is now functional as an automated processing chain. Here we present several recent LOFAR images that have been produced during the still ongoing commissioning period. These early LOFAR images are representative of some of the science goals of the commissioning team members.Comment: 11 pages, 6 figures. Accepted for publication in proceedings of "ISKAF2010 Science Meeting", PoS(ISKAF2010)05

Lofar Low-Band Antenna Observations of the 3C 295 and Bootes Fields: Source Counts and Ultra-Steep Spectrum Sources

We present Low Frequency Array (LOFAR) Low Band observations of the Boötes and 3C 295 fields. Our images made at 34, 46, and 62 MHz reach noise levels of 12, 8, and 5 mJy beam-1, making them the deepest images ever obtained in this frequency range. In t

Performance analysis of spatial filtering of rf interference in radio astronomy,” accepted for

Abstract—Radio astronomical observations are increasingly contaminated by man-made RF interference (RFI). If these signals are continuously present, then they cannot be removed by the usual techniques of detection and blanking. We have previously proposed a spatial filtering technique, where the impact of the interferer is projected out from the estimated covariance data. Assuming that the spatial signature of the interferer is time-varying, several such estimates can be combined to recover the missing dimensions. In this paper, we give a detailed performance analysis of this algorithm. It is shown that the spatial filter introduces a small increase in variance of the estimates (because of the loss in information) and that the algorithm is unbiased in case the true spatial signatures of the interferers are known but that there may be a bias in case the signatures are estimated from the same data. Some of the bias may be removed, and moreover, the bias only affects the auto-correlations, whereas the astronomical information is mostly in the cross-correlations. Index Terms—Algorithm performance, eigenvalues, interference cancellation, radio astronomy, RF interference, spatial filtering

APPLICATION OF ROBUST CAPON BEAMFORMING TO RADIO ASTRONOMICAL IMAGING

Radio telescopes based on large phased arrays form an interesting application area for array signal processing. LOFAR is a large low frequency (10Mz–240MHz) array consisting of 13,000 antennas grouped into 50 stations, currently under construction in the Netherlands. Data from a 60-element test station of LOFAR is available to evaluate the performance of calibration and imaging algorithms. In this paper we apply the Robust Capon Beamformer (RCB) to make images of the sky from measured data, and compare them to the classical Fourier-based images. The RCB takes uncertainty in the calibration into account. Instead of the usual spherical uncertainty sets, we have also derived a more constrained uncertainty set specifically for imaging with the RCB. The results are images with a higher dynamic range than classical or Capon beamforming. Additional simulations confirm that the images are more accurate. 1

Image Domain Gridding: a fast method for convolutional resampling of visibilities

In radio astronomy obtaining a high dynamic range in synthesis imaging of wide fields requires a correction for time and direction-dependent effects. Applying direction-dependent correction can be done by either partitioning the image in facets and applying a direction-independent correction per facet, or by including the correction in the gridding kernel (AW-projection). An advantage of AW-projection over faceting is that the effectively applied beam is a sinc interpolation of the sampled beam, where the correction applied in the faceting approach is a discontinuous piece wise constant beam. However, AW-projection quickly becomes prohibitively expensive when the corrections vary over short time scales. This occurs, for example, when ionospheric effects are included in the correction. The cost of the frequent recomputation of the oversampled convolution kernels then dominates the total cost of gridding. Image domain gridding is a new approach that avoids the costly step of computing oversampled convolution kernels. Instead low-resolution images are made directly for small groups of visibilities which are then transformed and added to the large uv grid. The computations have a simple, highly parallel structure that maps very well onto massively parallel hardware such as graphical processing units (GPUs). Despite being more expensive in pure computation count, the throughput is comparable to classical W-projection. The accuracy is close to classical gridding with a continuous convolution kernel. Compared to gridding methods that use a sampled convolution function, the new method is more accurate. Hence, the new method is at least as fast and accurate as classical W-projection, while allowing for the correction for quickly varying direction-dependent effects