Burst detection efficiency  In order to interpret our observed detection rate (upper limit) we need to know our efficiency for detection by the IFO and.

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Presentation transcript:

Burst detection efficiency  In order to interpret our observed detection rate (upper limit) we need to know our efficiency for detection by the IFO and analysis pipeline.  Evaluate efficiency by injecting simulated waveforms into the data stream, at early stage of the analysis pipeline.  Must make some (ad hoc) choice of waveforms to be simulated, try to cover parameter space.  Evaluate pipeline detection efficiency for different waveforms, amplitudes, IFOs, source directions, and different search algorithms (ETGs)  Simulations test the entire burst search analysis chain  Validate procedure by comparison with waveforms injected into the IFO itself (hardware injections).

Ad-hoc signals: Gaussians and Sine-Gaussians These have no astrophysical significance; But are well-defined in terms of waveform, duration, bandwidth, amplitude Crude “swept sine” S-G response SG 554, Q = 9 Astrophysical SN models (Zwerger-Muller, Dimmelmeier) parameterized by distance to source, will come later

Simulation procedure  Generate or read in simulated h(t) in LDAS/datacond  Convert from h(t) to LSC_AS-Q counts using time- dependent calibration info  Add bursts to data: 8 short (~100 msec) bursts in 360 secs of data, varying peak strain (amplitude), waveform  Send off to Event Trigger Generators in LDAS mpiAPI (Beowulf) and post-trigger processing  Inject ~80 signals for each amplitude and waveform, sampling ~20% of the full triple-coincidence dataset (where calibration information is available)

Efficiencies for Sine-Gaussians and Gaussians  Measure ETG power vs peak strain; apply thresholds  Evaluate efficiency  and statistical error vs peak strain (h o ) for each waveform / IFO / ETG  Fit smooth sigmoid curves, extract peak strain at 50% efficiency (b) tfclusters Noise triggers

Efficiency curves for waveforms, IFOs, sky averaging  Efficiency curves for different IFOs and waveforms  Average over source direction and polarization numerically, assuming triple- coincidence efficiency is product of single-coincidence efficiencies (no IFO noise correlations)  Divide event rate by efficiency to get exclusion region in rate vs strength

Exclusion limits, with calibration uncertainties Event rate vs peak strain with ~ 10% calib uncertainty