1. SKA System Overview (and some challenges) P. Dewdney Mar 22, 2010

2. 2 Dense Aperture Arrays Dishes Wide Band Single Pixel Feeds Phased Array Feeds Sparse Aperture Arrays 3-Core Central Region SKA Array & Receptor Technologies Artists’ Renditions from Swinburne Astronomy Productions

3. Sparse aperture arrays for the lowest frequencies LOFAR (Netherlands et al) LWA (USA) MWA (USA, Australia) Replication by Industry

4. EMBRACE Prototype for Dense Aperture Arrays Industry already involved in production. First Fringes

5. Dish Design and Prototyping CART 10 m composite prototype ATA 42x6m hydroformed dishes ASKAP 36x12m panel dishes Prototype 15 m composite dish KAT-7 7 x12m composite dishes

6. SPDO Multi-pixels at mid frequencies with dishes + phased-array-feeds Chequer-board phased array (ASKAP, Australia) ASKAP, Australia Vivaldi arrays DRAO Canada APERTIF (Astron, NL) ASKAP chequer board array

7. Offset design M. Fleming

8. rms error: 0.25 mm Final Mould Alignment Mold-Based Production Industry Involvement in Production

9. SPDO System Block Diagram 9

10. SPDO Receptor Framework 10

11. SPDO Systems Analysis External interfaces (E) System Context

12. 12 u-v Coverage (Configuration Task Force) Millenaar, Bolton et al. ~800 km 20 km

13. SPDO Investigations of options for long baselines: –Purchase bandwidth; obtain dark fibre; construct bespoke network? Routing of fibre between receptors. Signal Transport & Networks McCool, Grigorescu ~300 km ~5 km Industry Involvement

14. SPDO Example Signal Processing Overview 14 of 35 Sparse AAs + 250 Dense AA + 2000 15-m dishes with SPFs ~10 18 MACs ~5 x10 17 MACs ~10 18 MACs (WAG) W. Turner Industry Involvement

15. SPDO Compute Requirements for Dish-based Version of SKA * From correlator with 10 5 chans out, ~14000 input data streams, dumped every 200 ms. Software Development Algorithms for imaging undergoing rapid changes, especially for the new low frequency instruments (e.g. LOFAR). SKA may require developing new algorithms (and ultimately code), for calibration and imaging, as well as time-domain research. Optimisation for multi-core (1000’s) will also be a challenge. Central Computing Facility (Example) Based on Input data rate* 44 x 10 12 Byte s -1 av’ge from correlator (4-Byte real’s) Imaging Processor 110 Pflops @ 10 4 flops / input number (EVLA Memo 24) Archive 0.1 to 1 ExaByte Industry Involvement

16. SPDO System Technical Cost Drivers 16

17. 17

18. SPDO Through out the SKA, power consumption is a major issue: On-site –Concentrated loads at the centre. –Distributed loads (100’s of km from centre). –Cooling of equipment is difficult in a desert environment. Off-site –Probably a large computing load (Concentrated). Reduction of power consumption and optimisation of the power network will be features of design everywhere. –SKA performance may be power limited. Power Consumption

19. SPDO Imaging Dynamic Range (DR) Don’t want to build a supersensitive (high A/T sys or SS) telescope: –then find that it hits a limit after a few hours of integration, which is then irreducible because of systematic errors. –Requirements may vary, but DR is not just an issue for one science program. High DR is a system issue. –need to consider the whole signal chain, signal processing and imaging as a system. Current System Specification –1000 hours integration on a field. –~74 dB at 1.4 GHz.

20. SPDO Potential Limits to (DR) 1. Cannot model and calibrate systematic effects (errors) that are not fully understood. 2. Degrees of Freedom –Cannot solve for more parameters than there is information to support. –Information theory provides a fundamental basis for evaluating combinations of measurements, assumptions, and a-priori information.  Theory originally arose from studies of the amount of information that can be transmitted over a “noisy channel”. –Information theory provides guidance on optimum use of information, but does not provide guidance on actually understanding sources of errors. –Errors with direction-dependency, frequency-dependency or time-dependency add greatly to the number of parameters to be solved for.

21. SPDO Potential Limits to (DR) 3. Time Variability –All analog systems “drift”.  e.g. Gains of amplifiers are functions of temperature.  e.g. Switching levels and sample intervals in A/D converters vary in complex, non-random ways.  Not everything can be digital: antennas, receivers. –Digital systems do not drift.  But they are subject to bit errors at a low level. –Characteristic system drift times cannot be too short. 4. Calibration Signal-to-noise –Noise on calibrations imparts noise to images. –Calibrations subject to systematic errors too.

22. SPDO For a fixed-cost telescope, we have a fundamental design question: Where to put the money? –Do we design extremely robust sub-systems (antennas, receivers, correlators, etc.), whose characteristics are well- known and stable? –Do we design less expensive sub-systems and put funds into back-end computing instead, to calibrate and correct for upstream defects and time-variable errors? Major aspect of system design and optimization –Probably have to do both things for an extreme sensitivity telescope. –Must also err on the side of investing in difficult to upgrade sub-systems (e.g. antennas, AA’s). Cost vs DR

23. SPDO Must invest in difficult to upgrade sub-systems (e.g. antennas, AA’s) – from previous slide. Chick & Egg How do we qualify the SKA analog components at very high sensitivity (i.e. high DR)? Build the SKA so we can get enough sensitivity to qualify the components. The purpose of the receptor verification programs is to break the loop, and qualify the receptors in the best way possible.

24. SPDO Inverse Problem 24 It’s all about beam characteristics, not the type of receptor. 74 dB DR specification –For an a priori antenna design this is impossible to meet on its own. –e.g. pointing stability would have to be  25 million th of a beam.  Recovered pointing should meet the spec. Fortunately there are powerful modelling & calibration techniques to solve for beam characteristics as system parameters, while simultaneously solving for the image. – Depends on  being able to model systematic effects,  having more equations with measured parameters than unknowns,  signal-to-noise of calibration. But this does not tell us how to set the antenna specifications. –We will have to start with an informed guess. –Building an antenna for 1.4 GHz using the “usual” specs for 12-15 GHz is a good “rule-of-thumb”.

25. SPDO Approximate Verification Work Flow 25 TDP DVA-1 Program

26. SPDO System Performance Analysis 26

27. SPDO Beam Measurements – Input to Model 27 Stability in all wind/solar conditions is the key. What is the characteristic timescale of change? What does this depend on? Is it predictable?

28. SPDO 28 Potential Test Setup for SKA antenna Mosaic map pre-observed. Calibrator: o On-axis for the array. o Half-power point for SKA antenna.

29. SPDO EVLA 3C147 Deep Field @ 1440 MHz 12 antennas, 110 MHz bandwidth, 6 hours integration Fidelity ~ 400,000:1 Peak/rms ~ 850,000:1 (59 dB) The artifacts are due to non- azimuthal symmetry in the antenna primary beams. –Illustrates the need for advanced calibration/imaging software. 29 First Null Primary Beam Half Power Need Reasonably High-Fi Maps of Field Sources Perley et al

30. SPDO Imaging Dynamic Range Budget Visibility on baseline m-n Visibility-plane calibration effect Image-plane calibration effect Source brightness (I,Q,U,V) Direction on sky: ρ Basic imaging and equation for radio interferometry (e.g. Hamaker, Bregman, & Sault et al. 1996): Key contributions Robust, high-fidelity image-plane (ρ) calibration: –Non-isoplanatism. –Antenna pointing errors. –Polarized beam response in (t,ω), … Non-linearities, non-closing errors Deconvolution and sky model representation limits Dynamic range budget will be set by system design elements. (Bhatnagar et al. 2004; antenna pointing self- cal: 12µJy => 1µJy rms) From Athol Kemball

31. SPDO Site conditions: –As similar to actual sites as possible. –Strong solar, large day-to-night temperature changes. –Wind, dust. –Test conditions must encompass as many as possible of these effects. Beam parameters include polarisation properties. –Orthogonality, stability. Stability across frequency and tuning ranges: –Beamshape stability with frequency. –Frequency dependence of scattering and sidelobes. Other analog components: Bandshape, RF gain components, Analog-to-digital converters. Understanding the behaviour of these components will be very important. Best if already field-qualified, but at least bench qualified. Other Aspects 31

32. SPDO 32 Beam Rotation, DR, Processing Cost How much field must we process? Science FoV is product. Processed FoV is dross. Undersampled area: potential source of artifacts that could be costly but not necessarily impossible to remove. Rotation of Beam Pattern on Sky Mechanical de-rotation possible for some dish designs. Axisymmetric beams require fewer parameters to specify. Avoiding the zenith region slows rotation (>12 deg away slows rotation to <0.5 deg/min).

33. SPDO Must be able to tell that the receptors are on track to achieving the predicted performance as they are put into production. Requires end-to-end capability from the outset. May require several pauses, while evaluation is done on a sub-set of receptors. Continuous Evaluation persists through to the end of Phase 2. Follow-up During Roll-Out Continuous Evaluation 33

34. End 34