SKA System Overview (and some challenges)

P. Dewdney

Mar 22, 2010

2

Dense Aperture Arrays Dishes

Wide Band Single Pixel F

eeds

Phased Array F

eeds

Sparse Aperture Arrays

3-Core Central Region

SKA Array & Receptor Technologies

Artists’

Renditions from Swinburne Astronomy Productions

Sparse aperture arrays for the lowest frequencies

LOFAR (Netherlands et al)

LWA (USA)

MWA (USA, Australia)

Replication by Industry

EMBRACE Prototype for Dense Aperture Arrays

Industry already involved in production.

First Fringes

Dish Design and Prototyping

CART

10 m composite prototype

ATA42x6m hydroformed

dishes

ASKAP

36x12m panel dishes

Prototype 15 m composite dish

KAT-77 x12m composite dishes

SPDO

Multi-pixels at mid frequencies with dishes + phased-array-feeds

Chequer-board phased array (ASKAP, Australia)Chequer-board phased

array (ASKAP, Australia)

ASKAP, Australia

Vivaldi arraysDRAO Canada

APERTIF (Astron, NL)

ASKAP

chequer board array

Offset design

M. Fleming

rms error: 0.25 mm

Final Mould Alignment

Mold-Based Production

Industry Involvement in Production

SPDO

System Block Diagram

9

SKA Conceptual

Block Diagram

RegionalScience

Centre(s)

RegionalEngineering

Centre(s)

ScienceComputing

Facility

OutlyingStation

OutlyingStation

OutlyingStation

OutlyingStation

OutlyingStation

Dense Aperture

Array

Operationsand

MaintenanceCentre

SignalProcessing

Facility

HighLow

Sparse Aperture Array

On Site

SKA HQ(Off Site)

Global

High Performance ComputingData Storage

Digital Signal ProcessingBeamformingCorrelation

Outlying stationson spiral arm

(only one arm is shown)

Wide band single pixel

feeds (WBSPF)

Phased array feeds(PAF)

Dish Array

SPDO

Receptor Framework

10

SparseApertureArrays

DenseApertureArrays

WBSPF

70MHz 10GHz300MHz 700MHz 2GHz

10km

180km

3000+km

= potentiometer

PAFArrays

<1 km

SPDO

Systems AnalysisSKA Conceptual

Block Diagram

RegionalScience

Centre(s)

RegionalEngineering

Centre(s)

ScienceComputing

Facility

OutlyingStation

OutlyingStation

OutlyingStation

OutlyingStation

OutlyingStation

Dense Aperture

Array

Operationsand

MaintenanceCentre

SignalProcessing

Facility

HighLow

Sparse Aperture Array

On Site

SKA HQ(Off Site)

Global

High Performance ComputingData Storage

Digital Signal ProcessingBeamformingCorrelation

Outlying stationson spiral arm

(only one arm is shown)

Wide band single pixel

feeds (WBSPF)

Phased array feeds(PAF)

Dish Array

External interfaces (E)

System Context

12

u-v

Coverage(Configuration Task Force)

Millenaar, Bolton et al.

~800 km

20 km

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

SPDO

Example Signal Processing Overview

14 of 35Sparse AAs

+ 250 Dense AA + 2000 15-m dishes with SPFs

Sparse AAChannelizer Full Stokes Correlator

Pulsar & Transient Processing

Timing

xx,xy,yx,yy

Multipier

Opto-Electrical

Conversion

Optical Rx

TimingMonitoring &

Control

Visibilities

PulsarCandidate

Data

SparseAA

Local Monitoring

IntegratorDelays

PulsarSearch

& TimingProcessing

Opto-Electrical

Conversion

Opto-Electrical

Conversion

Candidates

Opto-Electrical

Conversion

70 MHz 500 MHz

Full Stokes Correlatorxx,xy,yx,yy

MultipierVisibilities

Integrator

300 MHz 1 GHz

Opto-Electrical

Conversion

Opto-Electrical

Conversion

Dense AAChannelizer

Opto-Electrical

Conversion

Optical Rx

SPF Dish Delays

TBA10 G bit/s

OpticalConnections

1,000,00010 G bit/s

OpticalConnections

TBA10 G bit/s

OpticalConnections

250Dense AA

1280Dishes

Opto-Electrical

Conversion

Optical Rx Full Stokes Correlatorxx,xy,yx,yy

MultipierVisibilities

Integrator

1 GHz 10 GHz

Opto-Electrical

Conversion

4224010 G bit/s

OpticalConnections

40Stations

Delays

Opto-Electrical

Conversion

Poly-PhaseFilter

Dish WBSPFChannelizer

Poly-PhaseFilter

Poly-PhaseFilter ~1018

MACs

~1018

MACs

~5 x1017

MACs

~1018

MACs

(WAG)

W. Turner

Industry Involvement

SPDO

Compute Requirements for Dish-based Version of SKA

* From correlator with 105

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 1012 Byte s-1

av’ge

from correlator (4-Byte real’s)Imaging Processor 110 Pflops @ 104

flops / input number (EVLA Memo 24)

Archive 0.1 to 1 ExaByte

Industry Involvement

SPDO

System Technical Cost Drivers

16

Performance Attributes Cost Impact Principal Area Affected

Frequency Range (>2.5 Decades) Major Everything

Point Source Sensitivity (Ae /Tsys) Major Receptors

Survey Speed ((Ae /Tsys )2 * Ω) Major Receptors & Processing

Imaging Dynamic Range Major Receptors, Signal Processing,

Image Processing

Spectral Dynamic Range Medium Data Transport,Signal

Processing

Polarization Purity (103.x) Medium Receptors

Configuration (0 – 103.x km) Major Data Transport

Instantaneous Bandwidth (dependent on ν, Baseline) Medium Receptors, Signal Processing

Number of Frequency Channels Major Data Processing

Calibration Medium Receptors, image processing

Time-domain Capabilit ies (transients, pulsars) Medium Data processing

Sky Coverage (technology dependent) Medium – Major

Receptors

17

2000

4000

6000

8000

Ae

/ Tsy

s10

000

Surv

ey S

peed

107

108

109

1010

105

106

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

SPDOImaging Dynamic Range (DR)

•

Don’t want to build a supersensitive (high A/Tsys 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.

SPDOPotential 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.

SPDOPotential 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.

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

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.

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 millionth

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 onbeing 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”.

SPDO

Approximate Verification Work Flow

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TDP

DV

A-1

Pro

gram

Estimate Key Antenna

Specifications

Prototype Antenna Construction

First Level Qualification

Tests

DeepVerification &

Modelling Tests

System Performance Analysis & Model

Development

Minor Antenna Modifications

Adjustments to ModelDevelopPerformance Model

New PrototypeDesign

ProductionDesign

Yes

No

Highest “affordable”

quality

Manufacturability Up-Front

Requirement

Single-Antenna Tests

Time-Dependent, Parameterised

Behaviour Model

In conjunction with well

understood 2-D array.

System Model Required

SPDO

System Performance Analysis

26

Time-DependentMulti-Parameter

Behavioural Receptor Model(image & visibility planes)

Model Parameters

Deconvolution

A-Priori Knowledge & External Constraints

ImageCubes

Instrument Calibration

Visibility Data

Unmodeled Systematic

Errors

Iterations

Iterations – improve the model and/or modify the antenna.

Noise

Time scale matching system drift time scales.

“Known Unknowns”

“Known known”

Noise

Source Calibration

Noise

Can we reach dynamic range and other quality specs?

SPDOBeam 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?

Synthesised Beam (2-D, low sidelobe,

high sensitivity, stable)Beam from

Antenna Under Test

Degree

dB

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.

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 BeamHalf Power

Need Reasonably High-Fi

Maps of Field Sources

Perley et al

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

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

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).Processed FoV

Science FoV

Undersampled

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

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