Backend electronics for radioastronomy

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Backend electronics for radioastronomy

• G. Comoretto

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Data processing of a radioastronomic signal

• Receiver (front-end)
• Separates the two polarizations
• Amplifies the signal by 108
• 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
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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)

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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....)

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Total power

• 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

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Total power

• Sensitivity
• t integration time
• Df bandwidth or frequency resolution
• S total (receiver dominated) noise
• For modern receivers, 1/f gain noise dominant for
  t gt 1-10 s
• need for accurate calibration noise subtraction
  
• Added mark
• Correlating receiver
• On-the fly mapping
• Wobbling optics

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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)

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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 gt not adjustable

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AOS Array for Herschel - HiFi

• LiNb cell with 4 acoustic channels
• Instantaneous band 4x1.1 GHz (4 8 GHz)
• Resolution 1 MHz

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Spectroscopy Digital correlator

• Digital spectrometers
• Bandwidth determined by sampling frequency
• Max BW technologically limited, currently to few
  100MHz
• Reducing sampling frequency decreases BW gt
  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

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Spectroscopy FFT spectrometer

• FFT spectrometers
• Compute spectrum of finite segment of data
• Square to obtain power and integrate in time
• Complexity proportional to log2(N) gt 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

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Spectroscopy FFT spectrometer

• Poly-phase structure multiply (longer) data
  segment with windowing function gt very good
  control of filter shape
• Very high dynamic range (106-109) gt RFI control

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Interferometry

• Visibility function ltE1(t)E2(tt)gt
• 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

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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)
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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
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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

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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 _at_ 125 MHz BW
  each

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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