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SKADS: Array Configuration Studies Implementation of Figures-of-Merit on Spatial-Dynamic-Range. Progress made & Current status Dharam V. Lal & Andrei P. Lobanov (MPIFR, Bonn). HUGE task. To quantify imaging performance of the SKA. Some Terminologies.
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SKADS: Array Configuration StudiesImplementation of Figures-of-Merit on Spatial-Dynamic-Range Progress made&Current statusDharam V. Lal & Andrei P. Lobanov (MPIFR, Bonn)
HUGE task To quantify imaging performance of the SKA.
Some Terminologies • Figures-of-Merit Any parameter which is a measure of (u,v)-plane coverage; e.g., SDR, RMS noise levels, synthesized beam size, etc. • Spatial Dynamic Range The ratio of the largest adequately imaged structure and the synthesized beam
… Terminologies … • (u,v)-gap parameter OR u/u A measure of quality of the (u,v)-plane coverage characterising the relative size of “holes” in the Fourier plane [U2 – U1] / U2 for a circular (u,v)-coverage where, U2 and U1 are the (u,v)-radii of two adjacent baselines.
Figures of Merit Pixel fidelity SKA VLBI • Commonly used:– resolution, beam shape, sidelobe level, dynamic range, etc… • Additional: – spatial dynamic range, pixel fidelity Resolution Spatial dynamic range
Spatial Dynamic Range • Spatial dynamic range (SDR) – the ratio between largest and smallest adequately imaged scales – it measures, effectively, brightness sensitivity of an array on all scales. • SDR reflects a number of aspects of array design, including the type of primary receiving element (antenna), signal processing, and distribution of antennas/stations. • Array configuration: SDR can be expressed as a function of a „gap“, Du/u, between adjacent baselines (u1,u2): Du/u = (u2 – u1)/u2 (u2 > u1) • Uniform sensitivity is provided by Du/u = const
SDR Factors • Integration time: • FoV: • Channel bandwidth • UV-coverage Analytical estimate: SDR of SKA will not be limited by the uv-coverage if Du/u 0.1on all scales The goal is to derive more specific requirements from numerical testing.
Methodology • Generate test array (X,Y) for logarithmic (equiangular) spiral array configuration • Project this array on Earth’s surface and determine (Lat, Lon, Z) • Choose an appropriate input source model • RUN glish scripts in aips++ to obtain visibilities • Import these visibilities into AIPS and perform the mapping using IMAGR task. • Determine the “figures of merit”
Preliminaries • An arbitrary choice of source model • Observing 1.4 GHz Observing direction, RA 00:00:00 Dec +90:00:00 A RUN of 12 hrs
Experiment 1 • A station at origin • Three spiral arms • Five stations in each arm • Range of baseline • from 20 – 100m • to 20 – 5000m vary “Bmax/Bmin” & constant “N” • [U2 – U1] / U2
Experiment 1 … • Input group of source components six Gaussian components, typical size ~1 arcsec • Results from Dirty Map (Use AIPS task IMAGR) • 4k x 4k image size • each pixel 2 arcsec • Figures-of-Merit • … VLA D A
Experiment 2 (U,V) gap parameter [U2 – U1] / U2 i.e., fix “Bmax/Bmin” & vary “N”
Experiment 2 … • Results (Use AIPS task IMAGR) • 8k x 8k image size and each pixel being 3 arcsec • Figures-of-Merit dirty map CLEAN map
Experiment 2 … • Shortest spacings, a few 10s of metres ~degree • Longest spacings (5000m) ~arcseconds
Results • The behaviour of figures of merit and hence the SDR does not seem to have a simple dependence on u/u. • Close to small (u,v)-gap parameter values, the (nearly) linear relationship does not hold good. • We show that uv-gap parameter can be used to relate the (u,v)-coverage to the characteristics of the map.
Results … • These empirical solutions can be implemented into any proposed configuration. • We plan to use the SDR FoM to quantify imaging performance of: • KAT / MEERKAT, ASKAP, SKA – Phase I • Limitations of CLEAN deconvolution algorithm • Need new algorithms and parallelisation.