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Register a new userThe 2012 Interferometric Imaging Beauty Contest
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We present the results of the fifth Interferometric Imaging Beauty Contest. The contest consists in blind imaging of test data sets derived from model sources and distributed in the OIFITS format. Two scenarios of imaging with CHARA/MIRC-6T were offered for reconstruction: imaging a T Tauri disc and imaging a spotted red supergiant. There were eight different teams competing this time: Monnier with the software package MACIM; Hofmann, Schertl and Weigelt with IRS; Thiebaut and Soulez with MiRA ; Young with BSMEM; Mary and Vannier with MIROIRS; Millour and Vannier with independent BSMEM and MiRA entries; Rengaswamy with an original method; and Elias with the radio-astronomy package CASA. The contest model images, the data delivered to the contestants and the rules are described as well as the results of the image reconstruction obtained by each method. These results are discussed as well as the strengths and limitations of each algorithm.
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The Keynesian Beauty Contest is a classical game in which strategic agents seek to both accurately guess the true state of the world as well as the average action of all agents. We study an augmentation of this game where agents are concerned about revealing their private information and additionally suffer a loss based on how well an observer can infer their private signals. We solve for an equilibrium of this augmented game and quantify the loss of social welfare as a result of agents acting to obscure their private information, which we call the price of privacy. We analyze t
This article presents the results of the Model Checking Contest held at Petri Nets 2012 in Hambourg. This contest aimed at a fair and experimental evaluation of the performances of model checking techniques applied to Petri nets. This is the second edition after a successful one in 2011. The participating tools were compared on several examinations (state space generation and evaluation of several types of formulae - structural, reachability, LTL, CTL) run on a set of common models (Place/Transition and Symmetric Petri nets). After a short overview of the contest, this paper provides the raw results from the context, model per model and examination per examination.
The Murchison Wide-field Array (MWA) is a low frequency radio telescope, currently under construction, intended to search for the spectral signature of the epoch of re-ionisation (EOR) and to probe the structure of the solar corona. Sited in Western Australia, the full MWA will comprise 8192 dipoles grouped into 512 tiles, and be capable of imaging the sky south of 40 degree declination, from 80 MHz to 300 MHz with an instantaneous field of view that is tens of degrees wide and a resolution of a few arcminutes. A 32-station prototype of the MWA has been recently commissioned and a set of observations taken that exercise the whole acquisition and processing pipeline. We present Stokes I, Q, and U images from two ~4 hour integrations of a field 20 degrees wide centered on Pictoris A. These images demonstrate the capacity and stability of a real-time calibration and imaging technique employing the weighted addition of warped snapshots to counter extreme wide field imaging distortions.
We study the impact of the spread spectrum effect in radio interferometry on the quality of image reconstruction. This spread spectrum effect will be induced by the wide field-of-view of forthcoming radio interferometric telescopes. The resulting chirp modulation improves the quality of reconstructed interferometric images by increasing the incoherence of the measurement and sparsity dictionaries. We extend previous studies of this effect to consider the more realistic setting where the chirp modulation varies for each visibility measurement made by the telescope. In these first preliminary results, we show that for this setting the quality of reconstruction improves significantly over the case without chirp modulation and achieves almost the reconstruction quality of the case of maximal, constant chirp modulation.
Extremely high data rates expected in next-generation radio interferometers necessitate a fast and robust way to process measurements in a big data context. Dimensionality reduction can alleviate computational load needed to process these data, in terms of both computing speed and memory usage. In this article, we present image reconstruction results from highly reduced radio-interferometric data, following our previously proposed data dimensionality reduction method, $mathrm{R}_{mathrm{sing}}$, based on studying the distribution of the singular values of the measurement operator. This method comprises a simple weighted, subsampled discrete Fourier transform of the dirty image. Additionally, we show that an alternative gridding-based reduction method works well for target data sizes of the same order as the image size. We reconstruct images from well-calibrated VLA data to showcase the robustness of our proposed method down to very low data sizes in a real data setting. We show through comparisons with the conventional reduction method of time- and frequency-averaging, that our proposed method produces more accurate reconstructions while reducing data size much further, and is particularly robust when data sizes are aggressively reduced to low fractions of the image size. $mathrm{R}_{mathrm{sing}}$ can function in a block-wise fashion, and could be used in the future to process incoming data by blocks in real-time, thus opening up the possibility of performing on-line imaging as the data are being acquired. MATLAB code for the proposed dimensionality reduction method is available on GitHub.
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