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Coherent detection and reconstruction of burst events in S5 data

This study presents the coherent network analysis of burst events in the S5 data from the LIGO scientific collaboration. The objective is to provide robust model-independent detection algorithms, combine measurements from multiple detectors, and reconstruct the source coordinates and GW waveforms. Results show improved detection efficiency compared to the incoherent search method.

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Coherent detection and reconstruction of burst events in S5 data

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  1. Coherent detection and reconstruction of burst events in S5 data S.Klimenko, University of Florida for the LIGO scientific collaboration S5 data coherent network analysis coherent WaveBurst S5 results (all results are preliminary) Summary

  2. LIGO network S5a, Nov 17,2005 – Apr 3, 2006 live time 54.4 days, preliminary DQ is applied S5b, Apr 3, 2006 - Nov 17,2006 live time 112.1 days, science segments S5 (first year), Nov 17,2005 - Nov 17,2006 live time 166.6 days (x10 of S4 run) duty cycle 45.6% L1xH1xH2xGeo S5 (first year), Nov 17,2005 - Nov 17,2006 live time 83.3 days Analysis coherent WaveBurst pipeline frequency band 64-2048 Hz S5 run

  3. Objective of Coherent Network Analysis • Provide robust model independent detection algorithms • Combine measurements from several detectors • handle arbitrary number of co-aligned and misaligned detectors • confident detection, elimination of instrumental/environmental artifacts • reconstruction of source coordinates • reconstruction of GW waveforms • Detection methods should account for • variability of the detector responses as function of source coordinates • differences in the strain sensitivity of the GW detectors • Extraction of source parameters • confront measured waveforms with source models

  4. Likelihood for Gaussian noise with variance s2 k and GW waveform u: xk[i] – detector output, Fk – antenna patterns Find solutions by variation of L over un-known functions h+, hx Standard likelihood approach may produce unphysical solutions for h+, hx need constraints (PRD 72, 122002, 2005) detector response - total energy noise (null) energy detected (signal) energy Likelihood

  5. Wave frame h+,hx x z y detector frame x z Rz(y) q,j y Network response matrix • Dominant Polarization Frame observables are RZ(Y) invariant • Solution for GW waveforms satisfies the equation • g – network sensitivity factor network response matrix • e – network alignment factor

  6. noise variance noise variance network SNR g(q,f) g(q,f) eg(q,f) eg(q,f) Virtual Detectors detector output (DW) plus X+(q,f) • Any network can be described as two virtual detectors • Use “soft constraint” on the solutions for the hx waveform. • remove un-physical solutions produced by noise • may sacrifice small fraction of GW signals but • enhance detection efficiency for the rest of sources cross Xx(q,f) g L1xH1xH2 network e

  7. channel 1 channel 2 channel 3,… data conditioning: wavelet transform, (rank statistics) data conditioning: wavelet transform, (rank statistics) data conditioning: wavelet transform, (rank statistics) coincidence of TF pixels Likelihood TF map generation of coincident events generation of coherent events built in event consistency final selection cuts external event consistency final selection cuts Coherent WaveBurst ETG post- production • Similar concept as for the incoherent WaveBurst, but use coherent detection statistic • Uses most of existing WaveBurst functionality

  8. post-production cuts simulated G-noise S5 data • For real data the pipeline output is dominated by glitches • Glitches produce responses which are typically inconsistent in the detectors. • But how to quantify inconsistent detector responses? • waveform consistency tests based on • reconstructed energy of the detector responses • network correlation coefficient

  9. Waveform Consistency L1/H1 coincident glitch hrss=1.1e-21 network correlation 0.3 L1 time-shifted red reconstructed response hrss=7.6e-22 black band-limited TS hrss=7.6e-22

  10. Coincidence strategies <xi2> - total energy Ni – null (noise) energy • Coherent triggers are coincident in time by construction • Definition of a coincidence between detectors depends on selection cuts on reconstructed energy • Optimal coincidence strategies are selected after trigger production • loose: EH1+EH2+EL1>ET (same as 2L>ET  used by cWB) • double OR: EH1+EH2>ET && EH1+EL1>ET && EH2+EL1>ET • strict: EH1>ET && EH2>ET && EL1>ET Apr 2006 “single glitches” rate variation by 3 orders of magnitude “double glitches”

  11. injections glitches coherent energy & correlation • detected energy: in-coherent coherent Cij - depend on antenna patterns and variance of the detector noise xi , xj – detector output • network correlation require

  12. injections glitches: full band f >200 Hz 40% difference in efficiency frequency dependent threshold Effective SNR • average SNR • effective SNR

  13. S5 Rates Preliminary • rates f=64-2048 Hz f>200-2048Hz

  14. Detection efficiency for bursts • Use standard set of ad hoc waveforms (SG,GA,etc) to estimate pipeline sensitivity • Coherent search has comparable or better sensitivity than the incoherent search • Very low false alarm (~1/50years) is achievable Preliminary hrss@50% in units 10-22 for sgQ9 injections expected sensitivity for full year of S5 data for high threshold coherent search

  15. Adding GEO to the network • Expect similar or better sensitivity for L1H1H2Geo than for L1H1H2 network if GEO noise is fairly stationary • sort coherent triggers on value of SNR in the detectors • for example, call a trigger to be the L1 glitch if dominated by GEO dominated by L,H S5 S4

  16. Network rates • Performance of LIGO-GEO network on S4 data (see Siong’s talk) • S5 rates

  17. Preliminary High threshold coherent search set thresholds to yield no events for 100xS5 data (rate ~1/50 years) • - expected S5 sensitivity to sine-gaussian injections

  18. Reconstruction of burst waveforms H1/H2 coincident magnetic glitch hrss=2.4e-22 • If GW signal is detected, two polarizations and detector responses can be reconstructed and confronted with source models for extraction of the source parameters • Figures show an example of LIGO glitch reconstructed with the coherent WaveBurst event display (A.Mercer et al.)  powerful tool for consistency test of coherent triggers.  waveforms can be confronted with models for extraction of source parameters. L1 time-shifted hrss=4.5e-22 red reconstructed response black bandlimited TS hrss=4.5e-22

  19. coherent WaveBurst pipeline generate coherent triggers for one year of S5 data robust discrimination of glitches  extra-low false alarm at excellent sensitivity excellent computational performance: trigger production for 101 time lags takes 1 day. prospects for S5 un-triggered search combine measurements with L1xH1xH2 and L1xH1xH2xGeo networks analyze outliers and apply final DQ and veto cuts final estimation of the detection efficiency and rates analyze zero lag triggers  produce final result Summary & Plans

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