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Cyclic Imaging: Interferometric Detection and Localisation of Wideband Engineered Signals Ian Morrison ICRAR- Curtin 31 October 2018 Wide-field Wideband SETI Goal: real-time detection and localisation over the whole FoV Want: large FoV

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Cyclic Imaging:

Interferometric Detection and Localisation

  • f Wideband Engineered Signals

Ian Morrison

ICRAR- Curtin 31 October 2018

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Wide-field Wideband SETI

2

Ø Goal: real-time detection and localisation over the whole FoV Ø Want: 1.

large FoV for survey speed

2.

high sensitivity for deep searches

Ø Array telescopes provide wide FoV, but to obtain maximum

sensitivity, need either:

  • co

coherent tied-ar array beam ams, possibly thousands to tile the entire FoV, with separate detector pipelines on each beam output, or

  • in

interferometric ic im imag agin ing, which can detect compact sources at (nearly) the full array sensitivity

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Wide-field Wideband SETI

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Ø But a wide FoV image will contain thousands/millions of radio sources

– how to discriminate natural and engineered sources?

Image credit: B. Saxton (NRAO/AUI/NSF); ALMA (ESO/NAOJ/NRAO); NASA/ESA Hubble.

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Wide-field Wideband SETI

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Narrowband SETI

  • look for “unnaturally narrow” emissions

BUT – impractical to image at ultrafine freq resolution à beamforming + high-res FFT à thousands of beams (computationally intensive) but detector on each beam is easy

Wideband SETI

  • beamforming approach: requires the same beamformer computations, plus more

complex detector processing on each beam BUT – for wideband SETI the imaging approach comes back into play BUT – only viable if there’s a means to differentiate natural and engineered sources

à cy cycl clic c ima maging

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Cyclic Imaging

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Ø Conventional imaging maps sky brightness as a function of RA and Dec

  • usually Stokes I (power flux), or other Stokes parameters

Ø Cyclic imaging maps a different metric:- cy

cycl clos

  • station
  • narity
  • only sources whose emissions contain cyclostationary power are visible

Cyclostationarity

  • “a signal having statistical properties that vary cyclically with time” (Wikipedia)
  • those properties can relate to voltages, powers and/or higher-order moments
  • can apply to both coherent and incoherent emissions, but always there is

time-coherence and specific cycle frequencies à correlation between signal components spaced regularly in time

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Example Cyclostationary Sources

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Natural

Ø pulsars

  • emission resembles broadband Gaussian

noise with a time-varying power envelope that repeats on a characteristic timescale – the pulsar’s period

Engineered

Ø pulsed radar

  • regularly spaced bursts of power, typically

sinusoids, chirps or pseudo-noise – not necessarily coherent

Image Credit: Manchester, R.N. and Taylor, J.H., Pulsars, Freeman, 1977. Image Credit: Bob Muro, Boonton.

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Example Cyclostationary Sources

7

Engineered

Ø digitally-modulated

communications signal

  • can exhibit envelope cyclostationarity

(e.g. bandlimiting, repeating frame structure with header pattern)

  • BUT – envelope can be constant and

still there is correlation between different symbols (when there is a finite symbol alphabet)

  • NOTE – different cyclostationary

detection algorithms may not detect all forms of cyclostationarity!!

Image Credit: kvaser.com. Image Credit: rfcafe.com. Image Credit: evalidate.in.

unfiltered BPSK eye diagram filtered BPSK eye diagram

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Conventional vs Cyclic Imaging

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Conventional vs Cyclic Imaging

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Expect all to be known pulsars/RRATs

  • nly

cyclostationary sources that are static on the sky

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SLIDE 11

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Detecting Cyclostationarity

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Ø Various techniques, including: 1.

Cyclic spectroscopy

2.

Autocorrelation

3.

Symbol-wise autocorrelation (SWAC)

4.

Karhunen–Loève Transform (KLT) (or any principle component analysis)

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SLIDE 13

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Detecting Cyclostationarity

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Ø Various techniques, including: 1.

Cyclic spectroscopy

2.

Autocorrelation

3.

Symbol-wise autocorrelation (SWAC)

4.

Karhunen–Loève Transform (KLT) (or any principle component analysis)

Ø Appeal of SWAC:

  • naturally extends to interferometric regime (from auto- to cross-correlations)

à symbol-wise cross-correlation (SWCC) and “cyclic visibilities”

  • does not rely on power envelope fluctuations
  • maximises detection sensitivity for modulation types of interest
  • incoherent accumulation of SWCC detection metric

à no need to fully phase up and calibrate the array (for detection)

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Symbol-Wise Cross-Correlation (SWCC)

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Ø Array telescopes provide multiple samplings of the same signal

  • independent receiver noise
  • lower SNR than tied-array output

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Cyclic Imager for MWA – ARC Grant Proposal

Presentation Title (Edit in File > 'Page Setup’ > ‘Header/footer’) 22

Ø Grant application currently in preparation

  • Australian Research Council, Discovery Project scheme
  • for funding commencing January 2020

Ø Requesting ~US$300k over 4 years, including support for:

  • 1 x PhD student stipend
  • small cluster of GPU-accelerated compute servers

Ø Principal investigators: Ian Morrison, Greg Hellbourg, David Davidson, Randall Wayth (all Curtin University) Ø External collaborators: University of New Mexico - LWA

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Collaboration Opportunities

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  • 1. Provide design review input
  • 2. Contribute to simulations and design
  • 3. Contribute to implementation (GPU code)
  • 4. Contribute GPU server hardware to enable a more powerful MWA prototype
  • 5. Contribute GPU server hardware for a duplicate system on another telescope
  • 6. Provide feedback as a beta user
  • 7. Other?
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Summary

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Ø Cyclic imaging could provide a useful capability at any current or future array telescope, supporting

  • RFI mitigation
  • space situational awareness
  • wideband SETI

Ø Will enable the first high sensitivity wide-field wideband SETI surveys Ø CYCLONE: a planned MWA prototype system aimed at demonstrating the value of cyclic imaging and exploring alternative implementation approaches

  • bidding for an Australian government grant (PhD student and equipment)
  • seeking collaborators to contribute expertise, coding effort or equipment
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Questions?

ian.morrison@curtin.edu.au

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