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Ramesh BhatRamesh BhatCentre for Astrophysics & Supercomputing Centre for Astrophysics & Supercomputing
Swinburne University of TechnologySwinburne University of Technology
Time Domain Astronomy Meeting, Marsfield, 24 October 2011
Searching for Fast Transients with
Interferometric Arrays
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An Australia-India collaborative project
Developing new scientific capabilities for the GMRT Transient detection pipeline High time resolution pulsar science VLBI between GMRT and Australian LBA
Collaborating institutions: Swinburne, Curtin/ICRAR, CASS (Australia) National Centre for Radio Astrophysics (India)
Project team:Matthew Bailes (Swinburne) Ben Barsdell (Swinburne)
Ramesh Bhat (Swinburne) Sarah Burke-Spolaor (JPL)
Jayaram Chengalur (NCRA) Peter Cox (Swinburne)Yashwant Gupta (NCRA) Chris Phillips (CASS) Jayanti Prasad (IUCAA) Jayanta Roy (NCRA) Steven Tingay (Curtin) Tasso Tzioumis (CASS)
W van Straten (Swinburne) Randall Wayth (Curtin)
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In This Talk:
Searching for fast transients - important considerations
GMRT as a test bed instrument Transient detection pipeline Event analysis methodology
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Searching for fast radio transients: Important considerations
Detection sensitivity, survey speed, and search volume -- Figure of Merit (FoM)
Propagation effects: e.g. dispersion, scattering, and scintillation due to the intervening media
Parameter space to search for: DM, time scale; computational requirements
Radio frequency interference (RFI) -- a major impediment in the detection of fast transients!
Detection algorithms; candidate identification and verification strategies
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De-dispersion
DM = Dispersion Measure (in units of pc cm-3)
Dispersion smearing can be quite severe at low obs frequencies
Processing will involve searching over a large range of dispersion measure (DM)
Low frequencies will require very fine steps in DM (e.g. ~1000 trial DMs @325 MHz)
Incoherent dedispersion: channelise data, shift and align the channels, then sum
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Searching for “events” in the time - DM parameter space
Detections of single pulses from J0628+0909
Standard search strategy: Dedispersion + matched filtering
Each “event” is characterised by its amplitude, width, time of arrival and dispersion measure
(DM)
Matched filtering
Time domain clustering
Matched filtering
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Observational Parameter Space
S (x, t, , )x : Location of the station
: Direction on sky
t : Time domain
: Radio frequency
RFI is site-specific & direction dependent: function of x and Effective use of “coincidence” or “anti-coincidence” filters
Celestial transients vs. RFI:
• May have similar -t signature (e.g. swept-frequency radar and pulsars)
• Will have very different occupancy of x- space:
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Detecting fast transients: search algorithms and strategies
PSR J1129-53 - an RRAT discovered by Burke-Spolaor & Bailes (2010)
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Transient Exploration with GMRT
30 x 45m dishes, collecting area ~ 3% SKA Modest number of elements, long baselinesAdvent of GMRT software backend (GSB) Demonstration of multibeaming across FoV Superb event localisation capabilities (~5”)Computational requirements are significant, however
affordable
GMRT makes an excellent test-bed for developing the techniques and strategies applicable for next-
generation (array type) instruments
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Considerations for sub-arraying: False alarm
probabilities
N independent elements Multiple sub-arrays, p = N/nIncoherent combination
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14 km
1 km x 1 km
RFI environment is known to vary significantly across the array; e.g. between the arms; between the central square and the arms (east, west, south)
Considerations for sub-arraying: RFI environment
Local RFI sources: • TV boosters• Cell phone towers• Power lines
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Antenna locations are marked in red
Locations of RFI sources are marked in blue
courtesy: Ue-Li Pen
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+
GMRT software backend (GSB)GMRT + configurability
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Transient Detection Pipeline for GMRT
Real-time processing and Trigger generation + Local recording of Raw Data
GMRT array GSB cluster Transient Detector Trigger Generator@ 2 GB/sec
512 MB/sec
(Ndm/Nchan) x 64 MB/sec
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Salient features of GMRT transient project
The GMRT + GSB combination offers some unique features for efficient transient surveys at low radio frequencies Long baselines: powerful discrimination between signals of
RFI origin vs celestial origin (via effective coincidence filtering)High resolution imaging: event localization (~ 5”-10”) possible
through imaging the field of view and/or full beam synthesisSoftware phasing (offline): sensitive phased array beams
toward candidate directions (~5 x sensitivity); base-band data benefits (e.g. coherent de-dispersion)
Search strategy: commensal mode with other observing programs; real-time processing and local recording
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Pilot transient surveys with the GMRT
Primary goals: Technical development Efficacies at low frequencies
Survey region: -10o < l < 50o , | b | < 1o @ 610 -10o < l < 50o, 1o < | b | 3o @ 325
Data recording Software backend’s “raw dump”
DR = 2 x 30 x (32 MHz)-1 x 4 bpsData from the surveys are used to
develop the transient processing and the event analysis pipelines
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Transient Detection Pipeline
RFI + quality checks
Form N Sub-arrays
De-dispersion
Transient detection
Event identification
Coincidence filter
Trigger generation
Data extraction
Event analysis
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Examples from the pipeline: a real astrophysical signal
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Examples from the pipeline: spurious signals (local RFI)
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Spectral Kurtosis Filter for RFI excision: Implementation on CASPSR
Andrew Jameson (Swinburne)
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Need for high resolutions in time, frequency and DM space
Signals can be as short as tens of micro seconds at GMRT frequencies Maximum achievable time resolution ~ 30 us with the current pipeline
An example from the GMRT transient detection pipeline (mode: 7 sub-arrays)
A Giant Pulse from Crab Pulsar at GMRT 610 MHz, Time duration ~ 50 us
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Processing Requirements
Benchmark with current software: data at full resolution (30 us, 512 channel FB) 15 x real-time on a dual quad-core Dell PE1950 equivalent to 133 Gflops (theoretical)
Net processing requirement: 15 x 133 Gflops = 2 Tflops (per beam!)
Possible (practical) solutions: Data down sampling (degrading resolution in f-t) by a factor 4 4 machines per beam OR 16 machines for 4 subarray beams Alternatively, 4 x GPUs, each of 0.5 Tflops
De-dispersion (searching in DM parameter space) is the most computationally intensive part of the pipeline
30 us, 512-channels
16 bit data samples
DM range: 0 - 500
tolerance level: T1.25
GPU dedispersion code by Ben Barsdell (Swinburne)
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Considerations for the real-time system: false positives and RFI signals
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Considerations for the real-time system: (false positives + RFI) + real
signal
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Event Analysis (offline) Pipeline
Localisation of the event on sky + phasing up + further checks
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FLAGCAL: A flagging and
calibration package
Description of the FLAGCAL pipeline in Prasad & Chengalur (2011)
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Snapshot imaging for event localisation
Currently FLAGCAL + AIPS; will soon be integrated into the main event analysis pipeline
“Dirty” imageSingle pulse from J1752-2806
“dirty” image
After cleaning and self-cal
Signal peak ~ 0.27 Jy
rms ~ 6 mJy; beam ~ 59” x 10”
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Example from Event Analysis Pipeline
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Summary and Concluding Remarks
Searching for fast transients with multi-element instruments involve several considerations and challenges; propagation effects, RFI, signal processing, etc.
The GMRT makes a powerful test bed for developing and demonstrating novel transient detection techniques and methodologies applicable for next-generation (LNSD type) instruments such as ASKAP
Transient detection pipeline for GMRT - development nearly complete; the commensal surveys to start by early 2012; the system will be extended to larger bandwidths
The VLBA and GMRT based efforts will help demonstrate the advantages of multiple stations and long baselines for transient exploration; valuable lessons for the SKA-era