Modeling Sky recovery problem in Modern Radio Telescopes imaging pipeline in compressive sensing framework

Radio telescopes take measurements from electromagnetic radiation in space and thereafter provide sky images based on these measurements in fine details. Modern radio telescopes are composed of thousands of dishes, stations that forms interferometer and a central processor. Hierarchically, antennas at various locations on the ground records radio waves coming from the sources in space, and then sent those to respective stations. Antennas geographically close to one another are grouped in stations and beamforming at station level will lead to directional data transmission thus improve signal-to-noise ratio (SNR). Interferometers first estimate cross-correlation between the time series measurements so-called visibilities, and finally central processor estimates the sky image with a specified imaging technique. In this work, we will model the telescope pipeline in compressive sensing framework. As baseline, see [1, 2].

Supervised by Nezihe Merve Gürel


  1. [1] Gurel, N. M., Kara, K., Stonajov, A., Smith, T., Alistarh, D., Puschel, M. & Zhang, C. (2018). Compressive Sensing with Low Precision Data Representation: Theory and Applications. arXiv preprint:1802.04907.
  2. [2] Wiaux, Y., Jacques, L., Puy, G., Scaife, A. M. M. & Vandergheynst, P. (2008). Compressed sensing imaging techniques for radio interferometry, arXiv preprint: 0812.4933.