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
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
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
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
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
25
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
33
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