K. Zarb Adami

MITEoR: a scalable interferometer for precision 21 cm cosmology

We report on the MIT Epoch of Reionization (MITEoR) experiment, a pathfinder low-frequency radio interferometer whose goal is to test technologies that improve the calibration precision and reduce the cost of the high-sensitivity 3D mapping required for 21 cm cosmology. MITEoR accomplishes this by using massive baseline redundancy, which enables both automated precision calibration and correlator cost reduction. We demonstrate and quantify the power and robustness of redundancy for scalability and precision. We find that the calibration parameters precisely describe the effect of the instrument upon our measurements, allowing us to form a model that is consistent with

Real-time, fast radio transient searches with GPU de-dispersion

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Implementation of a direct-imaging and FX correlator for the BEST-2 array

per degree of freedom < 1.2 for as much as 80% of the observations. We use these results to develop an optimal estimator of calibration parameters using Wiener filtering, and explore the question of how often and how finely in frequency visibilities must be reliably measured to solve for calibration coefficients. The success of MITEoR with its 64 dual-polarization elements bodes well for the more ambitious Hydrogen Epoch of Reionization Array (HERA) project and other next-generation instruments, which would incorporate many identical or similar technologies.

A Digital Broadband Beamforming Architecture for 2-PAD

The identification and subsequent discovery of fast radio transients using blind-search surveys require a large amount of processing power, in worst cases scaling as . For this reason, survey data are generally processed off-line, using high-performance computing architectures or hardware-based designs. In recent years, graphics processing units (GPUs) have been extensively used for numerical analysis and scientific simulations, especially after the introduction of new high-level application programming interfaces. Here, we show how GPUs can be used for fast transient discovery in real time. We present a solution to the problem of de-dispersion, providing performance comparisons with a typical computing machine and traditional pulsar processing software. We describe the architecture of a real-time, GPU-based transient search machine. In terms of performance, our GPU solution provides a speed-up factor of between 50 and 200, depending on the parameters of the search.

A Machine Learning approach for automatic land cover mapping from DSLR images over the Maltese Islands

A new digital backend has been developed for the BEST-2 array at Radiotelescopi di Medicina, INAF-IRA, Italy which allows concurrent operation of an FX correlator, and a direct-imaging correlator and beamformer. This backend serves as a platform for testing some of the spatial Fourier transform concepts which have been proposed for use in computing correlations on regularly gridded arrays. While spatial Fourier transform-based beamformers have been implemented previously, this is to our knowledge, the first time a direct-imaging correlator has been deployed on a radio astronomy array. Concurrent observations with the FX and direct-imaging correlator allows for direct comparison between the two architectures. Additionally, we show the potential of the direct-imaging correlator for time-domain astronomy, by passing a subset of beams though a pulsar and transient detection pipeline. These results provide a timely verification for spatial Fourier transform-based instruments that are currently in commissioning. These instruments aim to detect highly-redshifted hydrogen from the Epoch of Reionization and/or to perform widefield surveys for time-domain studies of the radio sky. We experimentally show the direct-imaging correlator architecture to be a viable solution for correlation and beamforming.

Optimal SKA antenna configuration using genetic algorithms

We describe an hierarchical, frequency-domain beamforming architecture for synthesising a sky beam from the wideband antenna feeds of digital aperture arrays. The development of densely-packed, all-digital aperture arrays is an important area of research required for the Square Kilometre Array (SKA) radio telescope. The design of real-time signal processing systems for digital aperture arrays is currently a central challenge in pathfinder projects worldwide. In particular, this work describes a specific implementation of the beamforming architecture to the 2-Polarisation All-Digital (2-PAD) aperture array demonstrator.

Dictionary based encoding of cosmological images

Abstract High resolution raster data for land cover mapping or change analysis are normally acquired through satellite or aerial imagery. Apart from the incurred costs, the available files might not have the required temporal resolution. Moreover, cloud cover and atmospheric absorptions may limit the applicability of existing algorithms or reduce their accuracy. This paper presents a novel technique that is capable of mapping garrigue and/or phrygana vegetation as well as karst or ground-armour terrain in photos captured by a digital camera. By including a reference pattern in every frame, the automated method estimates the total area covered by each land type. Pixel based classification is performed by supervised decision tree methods. Although the intention is not to replace traditional surface cover analysis, the proposed technique allows accurate land cover mapping with quantitative estimates to be obtained. Since no expensive hardware or specialised personnel are required, vegetation monitoring of local sites can be carried out cheaply and frequently. The developed proof of concept is tested on photos taken in thirteen different sites across the Maltese Islands.

A Real-Time Antenna Verification System

The Square Kilometre Array (SKA) is a radio telescope designed to operate between 70MHz and 10GHz. Due to this large bandwidth, the SKA will be built out of different collectors, namely antennas and dishes to cover the frequency range adequately. In order to deal with this bandwidth, innovative feeds and detectors must be designed and introduced in the initial phases of development. Moreover, the required level of resolution may only be achieved through a novel configuration of dishes and antennas. Due to the large collecting area and the specifications required for the SKA to deliver the promised science, the configuration of the dishes and the antennas within stations is an important question. This research investigates the applicability of machine learning techniques to determine an optimum configuration for the elements within an aperture array station. Genetic algorithms are primarily used to search a large space of optimum layouts. Fitness functions based on estimates of the main lobe to maximum side lobe ratio, the side lobes fall off rate, the main lobe area to side lobes area ratio as well as the kurtosis of residuals from polynomial fits of the main beam, are employed.

Design of PHAROS2 Phased Array Feed

The pioneering theory of Compressed Sensing (CS) provides a framework for ill-posed inverse problems and allows for the recovery of sparse signals from a set of measurements. Its applicability to astronomy datasets was recognised from its infancy. In this work, CS techniques are used to aid in the construction of an optimised dictionary that is capable of encoding cosmological images. A learning algorithm that automatically determines and adapts the size of the repository according to the provided training set, is presented. Use of the robust and fast StOMP ℓ1 minimization method is made for the recovery of sparse one dimensional signals. The results suggest that accurate reconstructions with very low residual errors can be obtained.