Backend electronics for radioastronomy

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24 Νοε 2013 (πριν από 3 χρόνια και 10 μήνες)

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Backend electronics for
radioastronomy

G. Comoretto

Data processing of a
radioastronomic signal


Receiver (
front
-
end
)


Separates the two polarizations


Amplifies the signal by ~10
8



Limits the band to a few GHz


Translates the sky frequency to a more manageable
range


The resulting signal is then processed by a
back end



Electric field E(t)


Power density S(f)

to
backend

Data processing of a
radioastronomic signal


Measure S as a function of time, frequency, polarization
status, baseline


Total power


Polarimetry


Spectroscopy


Interferometry


Pulsar (search and timing)


Record the instantaneous field E(t) for further
processing


VLBI/ Remote interferometry


Radio science


Composite of the above (e.g. spectropolarimetric
interferometry)



Signal conversion


IF output may be too wide


Difficulties of building wideband backends


Necessity of having several spectral points across
the IF bandwidth (e.g. for Faraday rotation)


Interest in a specific spectral region (e.g. line
spectroscopy)


Necessity to avoid contaminated portion of the IF
band


Baseband converters (BBC): select a portion of the IF
bandwidth and convert it to frequencies near zero


Each BBC followed by a specific backend (total power,
polarimeter, spectrometer, VLBI channel....)


Simplest observable: total integrated flux over the
receiver bandwidth






Filter: selects the frequency band of interest


Square law detector: diode (simpler, wideband) or
analog multiplier (more accurate, expensive, band
limited)


Integrator: sets integration time: time resolution vs.
ADC speed


ADC: converts to digital. Integrator & ADC are often
implemented as a voltage
-
to
-
frequency converter &
counter

Total power


Sensitivity:


t

= integration time


D
f

= bandwidth or frequency resolution


S

= total (receiver dominated) noise


For modern receivers, 1/f gain noise dominant for t > 1
-
10 s


need for accurate calibration & noise subtraction


Added mark


Correlating receiver


On
-
the fly mapping


Wobbling optics


Total power

Polarimetry


Dual polarization receiver:
vertical/horizontal or
left/right


Cross products give
remaining Stokes
parameters


Instrumental polarization:
30dB = 0.1%


Bandwidth limited by
avaliable analog multipliers


Need for coarse
spectroscopic resolution
(Faraday rotation)

Spectroscopy


Acousto
-
optic spectrometer:


signal converted to acoustic waves in a crystal


diffraction pattern of a laser beam focussed on a CCD


amplitude of diffracted light proportional to S(f)


Large bandwidth, limited (1000 points) resolution


Rough, compact design


All parameters (band, resolution) determined by
physical design => not adjustable

AOS Array for Herschel
-

HiFi



LiNb cell with 4 acoustic channels


Instantaneous band: 4x1.1 GHz (4


8 GHz)


Resolution : 1 MHz

Spectroscopy


Digital
correlator


Digital spectrometers:


Bandwidth determined by sampling frequency


Max BW technologically limited, currently to few 100MHz


Reducing sampling frequency decreases BW = > increased
resolution


Autocorrelation spectrometers (XF)


Compute autocorrelation function:


Fourier transform to obtain S(f)


Frequency resolution:


Signal quantized to few bits (typ. 2)


Complexity proportional to N. of spectral points

Spectroscopy


FFT
spectrometer


FFT spectrometers:


Compute spectrum of finite segment of data


Square to obtain power and integrate in time


Complexity proportional to log
2
(N) => N
large


Requires multi
-
bit (typ. 16
-
18 bit) arithmetic


Easy to implement in modern, fast FPGA, with HW
multipliers


Slower than correlator, but keeping pace


Polarimetric capabilities with almost no extra cost

Spectroscopy


FFT
spectrometer


Poly
-
phase structure: multiply (longer) data segment
with windowing function => very good control of filter
shape


Very high dynamic range (10
6
-
10
9
) => RFI control

Interferometry


Visibility function: <E
1
(t)*E
2
(t+
t
)>


Computed at distant or remote location: need for
physical transport of the radio signal


Directly connected interferometers


Connected interferometers with digital samplers
at the antennas and digital data link


E
-
VLBI: time
-
tagged data over fast commercial
(IP) link


Conventional VLBI: data recorded on magnetic
media


Accurate phase and timing control

Interferometry


Visibility computed on dedicated correlator or FFT
processor


Conventional correlator scales as (number of
antennas)
2


FFT (FX) scales as N


Must compensate varying geometric delay:


Varying sampler clock


Memory based buffer, delay


by integer samples


Phase correction in the


frequency domain


Due to frequency conversion,


varying delay causes


“fringe frequency” in the correlation

ALMA correlator (1 quadrant)

Digital vs. Analog Backend


All backend functions can be performed on a digital
signal representation


Current programmable logic devices allow to implement
complex functions on a single chip


Digital system advantages:


predictable performances


easy calibration


high rejection of unwanted signals
-

RFI


Better performances, filter shapes etc.


Easy interface with digital equipments

Example of a general
-
purpose full digital
backend

Digital vs. Software
Backend


Software backends (e.g. SW correlator) becoming
possible


e.g Blue Chip IBM supercomputer viable as LOFAR
correlator


Most Radio Science processing done on software


Computing requirements scale as a power of the BW


Dedicated programmable logic still convenient


1 FPGA: 50
-
500 MegaOPS, ~16 FPGA/board


MarkIV correlator (in FX architecture): 1.7 TeraOPS


EVLA Correlator: 240 TeraOPS

Digital Backend: Examples


ALMA Digital filterbank:


2 GHz IF input


32x62.5 MHz
independently tunable
BBC


General purpose board,
can be configured to
implement 16 FFT
spectropolarimeters @
125 MHz BW each

Digital Backend: Examples


VLBI dBBC:


1 GHz IF input


250 MHz output bandwidth


Directly interfaces with E
-
VLBI


BEE2 Berkeley system


1 GHz IF input


General purpose board, with library of
predefined components


System design and validation using MATLAB