Design issues and implementation challenges

1. Design issues and implementation challenges Paul Alexander and Peter Hall

2. Aim and scope • Concentrate on the design issues for SKA1 • SKA1 AA-low is a (transformational) world leading instrument • Essential to design for SKA1 • Consider how to transition to SKA2 • Identify issues which are independent of detailed design • Then consider issues which drive detailed design • Aim is to pose questions that we can aim to make progress on during the course of this meeting • Some questions should be answered • General point: • Transition from a research programme to an instrument project means we need to retire questions with an accountable path of how and why the decision was reached.

3. SKA-low • Excellent learning platforms in pathfinders • LOFAR, MWA, ... • Science, engineering, project management, operational lessons • Why optimize SKA-low? • Evolving science case • Possible new specification optimization • Pathfinders not scalable to SKA-1 • e.g. LOFAR x10 > SKA-1 budget • Rapid technology changes • Verify or change long-standing assumptions • Cost optimization funds new capabilities • More independent FoVs, increased time domain processing, ... • Actual SKA site conditions impact SKA-low design significantly

4. SKA-low design • SKA-low is part of bigger SKA system • Specifications flow from (updated) SKA Design Reference Mission • Performance/cost analysis must be done in SKA design environment • Cost must reflect “total cost of ownership” • SKA environment must capture key AA-lo issues • SKA operational model is critical to costing, e.g. • Simultaneity of SKA-low & SKA-mid operations • Data transmission, signal + post-processing • Data archiving • Site infrastructure constraints and costs • Including energy availability and cost • Support model, and lifetime costs (“maintenance”)

5. Top level issuesInsensitive to detailed design

6. Basic specifications • A low-frequency sparse aperture array with A/Tsys of up to 2000 m2/K • At what frequency is this optimised (100MHz?) ? • Operating at frequencies between 70 and 450 MHz • At what range of frequencies is this optimised • How tight are the constraints both scientifically and technically? • Array will be centrally condensed but some of the collecting area will be in stations located out to a maximum baseline length of 100 km from the core • What fraction of the collector is on longer baselines? • How large is the core?

7. Possible trade-offs (cost constrained design) • Built area vsFoV • More area, or more accessible and/or processed FoV? • Accessible bandwidth vs sensitivity • Fewer compromises in a narrower band array • Accessible bandwidth vs polarization capability, polarimetry performance • Processed FoV, bandwidth vs other parameters • Optimum investment in data transmission, DSP, computing • Investment level as a function of time • U-V coverage vs other parameters • More stations are costly (e.g. infrastructure, correlation) • Station numbers and size related to calibration strategy (esp. ionospheric)

8. Frequency range 6.5:1 • What frequency range must the array elements be designed/optimised for? • Approach 1: Observatory • Aim for best “average” or “uniform” response across the frequency range • Approach 2: Observatory, but prioritising EoR • Design antenna for good performance in EoR frequency range • What is the EoR frequency range 70 – 200 MHz? What about foregrounds? • Approach 3: EoR instrument with observatory function • Optimise design for EoR frequency range • Approach 4: Identify the technical difficulties and relax frequency range • 100-450 MHz is only 4.5:1

9. Sky coverage 45 degree scan SKA1 30 degree scan GC ALMA Circumpolar limit • Critical design driver for element • Observatory requirement – large sky coverage  lower gain antenna larger scan angle of 45 degrees. What is largest scan angle we would like? • Dedicated EoR experiment perhaps require smaller scan angle  higher gain antenna possible

10. Aside SKA1 specification is for an amazing instrument ~ 1 order of magnitude in sensitivity ~ 2-3 orders of magnitude in survey speed

11. Sensitivity requirement • Design specification: 2000 m2/K • f Tsky (K) Aeff (km2) • 100 MHz 988 2.1 • 150 MHz 350 0.70 • We will be building approximately a square kilometre of collecting area • What sensitivity do we require across the band? • Very dependent on the frequency at which the array becomes sparse • Major impact on element design

12. Tailoring the AA system 10000 1000 Sparse AA-lo Fully sampled AA-hi 100 Aeff Tsky Becoming sparse Sky Brightness Temperature (K) Aeff / Tsys (m2 / K) 10 Aeff/Tsys AA frequency overlap fAA fmax 1 Dishoperation 100 Frequency (MHz)

13. SKA1 sensitivity model 2000 m2/K at 100 MHz Trec = 60 K AA sparse above 150 MHz

14. Tsys across the band • Matching • f Tsky (K) • 100 MHz 988 • 150 MHz 350 • 180 MHz 221 • 210 MHz 150 • 240 MHz 106 • 400 MHz 29 • Trec important even at 200 MHz • Dominant at upper end of band • True low-noise LNAs still important Challenges: “Matching” across the band to ensure Trec dominated at upper end and Tsky at lower

15. SKA1 survey speed 2000 m2/K at 100 MHz Trec = 60 K AA sparse above 150 MHz NB gives 100 sq degrees across band

16. Survey speed • What survey speed do we require at fixed Aeff/Tsys? • Direct implication for cost of correlator and post-correlator processing • See next section for possible trade off • Upgrade path • Increasing survey speed is perhaps easiest designed in upgrade path for AA-low

17. Data rate • Data rate and survey speed intimately linked • Review basic design equations • Re-write in terms of FoV and total collecting area D B NsStations

18. SKA1 data rates and configuration • AA Line experiment 50 AA-low stations • 100 sq degrees, 10000 channels over 380 MHz bandwidth • 3.3 GS/s • Issues • What data rate can we process? • Trade UV coverage (Ns) for FoV and hence survey speed (W) • Line vs continuum requirements • What is the longest baseline • What temperature sensitivity do we need and on what scales • Defines filling factor in the core

19. SKA1 configuration • Ideally – do not design in these trade-offs • Need to consider evolution of processing capability in designing configuration • Or even repositionable antenna positions?!

20. Station and element design • One or two elements? • Many aspects to this – see later • How sparse can the station be? • Side lobes even for a random configuration when very sparse • Complicates imaging, and increases Tsys • Station size? • Increasing D  reduced UV coverage, reducedprocessing load, less complicated ionospheric model, move DSP from correlator to station B/F

21. Station design regular triangular sparse thinned circular random Embedded element pattern Random minimum l / 2 Random minimum 2 l Nima and Eloy

22. Configuration, station design and SKA2 • Is SKA1 a subset of SKA2 • Should we compromise the design (and hence science return) of SKA1to ease implementation of SKA2? • Optimum SKA1 AA-low core may have f ~ 0.5 Dcore ~ 1km. • SKA2 AA-low core is larger with f ~ 1 • Almost certainly need to reposition elements on SKA1 SKA2 Do not compromise design of SKA1 maximise science return for SKA1 & accept additional cost in SKA2

23. SKA information and data system Grid science reduction and visualisation Science proposal Observation definition M&C database Cloud store Global and local sky model Monitor and Control system Data excision Correlator Data excision Data product distribution Calibration loop Collectors Imaging processor Science product archive Data routing Visibility processors Local science reduction Hierarchical station beam former

24. Processing – how much and where? • For a given sensitivity and survey speed we can decide where and how to do the processing • Beam forming vs correlation  survey speed vs imaging fidelity? • Physical location of processing • Physically distribute processing only if it leads to a reduction in data rate

25. Processing – how much and where? • For a given sensitivity and survey speed we can decide where and how to do the processing • Beam forming vs correlation  survey speed vs imaging fidelity? • Physical location of processing • Physically distribute processing only if it leads to a reduction in data rate – e.g. Station beamformer

26. Specific Design and Implementation Issues

27. Element and communications • Can we cover band with a single element? • Where are the compromises? • Can we afford two elements? • Where do we digitise • Link, power consumption, lightning protection ... • What is the communication link? • Cost, calibratability and lightning protection • How is the element powered? • Cost, sustainabilty, manufacturability and deployability • What is the element assembly and how are they deployed? • Cost, sustainabilty, manufacturability and deployability

28. Station B/F and correlator • Station B/F • What is, and can we meet the power budget with an all digital design? • Do we deploy ASICs in the SKA1 design? If so what are the timescales for development cycle. • Note cost of Station B/F dominated by number of elements not how they are deployed (e.g. Station size) • Internal station correlation for calibration? • Correlator • Is a software correlator possible or desirable for SKA1 or commissioning?

29. Post-correlator processing What is our system concept for SKA1 processing? Is the post correlator processing a single Peta-scale machine or machine designed for our data flow? Our problem is highly parallel in places and we could deploy a “UV-processor” Need to be sure of processing model to go down this route, but can deliver more Flops cheaply Single-pass algorithms will reduce cost  do we want to restrict ourselves in this way?

30. Cost control

31. SKA-low implementation challenges • Low capital cost • N x 100,000 active antennas  integrated, reproducible • Strong incentive to incorporate Design for Manufacture early in development cycle • Low operating cost • Easily dominates capital cost over life of SKA • Reliability and maintainability are crucial • Probably dominant aspect of designing “outdoor” portion SKA-low • Robust system is essential • Damage limitation strategies (lightning etc), intelligent and resilient processing • Low deployment cost (next slide) • Data processing and archiving prominent in SKA Observatory plan • EMC • SKA-low is especially vulnerable to poor EMC practices, or poor site management with respect to RFI

32. Deployment challenge • 300,000 elements (or tiles) deployed over 2 years • 1 element/tile every minute! • Connectivity and commissioning need to keep pace with deployment • Parallel, industrialized deployment needed • … and during pre-construction • Substantial site specific and environmental issues • “Design for deployment” essential • Results in highly modular, maintainable design