ramesh bhat centre for astrophysics & supercomputing swinburne university of technology

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

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)

In This Talk:

Searching for fast transients - important considerations

GMRT as a test bed instrument Transient detection pipeline Event analysis methodology

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

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

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

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:

Detecting fast transients: search algorithms and strategies

PSR J1129-53 - an RRAT discovered by Burke-Spolaor & Bailes (2010)

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

Considerations for sub-arraying: False alarm

probabilities

N independent elements Multiple sub-arrays, p = N/nIncoherent combination

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

Antenna locations are marked in red

Locations of RFI sources are marked in blue

courtesy: Ue-Li Pen

+

GMRT software backend (GSB)GMRT + configurability

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

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

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

Transient Detection Pipeline

RFI + quality checks

Form N Sub-arrays

De-dispersion

Transient detection

Event identification

Coincidence filter

Trigger generation

Data extraction

Event analysis

Examples from the pipeline: a real astrophysical signal

Examples from the pipeline: spurious signals (local RFI)

Spectral Kurtosis Filter for RFI excision: Implementation on CASPSR

Andrew Jameson (Swinburne)

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

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)

Considerations for the real-time system: false positives and RFI signals

Considerations for the real-time system: (false positives + RFI) + real

signal

Event Analysis (offline) Pipeline

Localisation of the event on sky + phasing up + further checks

FLAGCAL: A flagging and

calibration package

Description of the FLAGCAL pipeline in Prasad & Chengalur (2011)

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”

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