S.Klimenko, G Z, December 2006, GWDAW11 Coherent detection and reconstruction of burst events in S5 data S.Klimenko, University of Florida for the LIGO scientific collaboration l S5 data l coherent network analysis l coherent WaveBurst l S5 results (all results are preliminary) l Summary

S.Klimenko, G Z, December 2006, GWDAW11 S5 run l LIGO network  S5a, Nov 17,2005 – Apr 3, 2006  live time 54.4 days, preliminary DQ is applied  S5b, Apr 3, Nov 17,2006  live time days, science segments  S5 (first year), Nov 17, Nov 17,2006  live time days (x10 of S4 run)  duty cycle 45.6% l L1xH1xH2xGeo  S5 (first year), Nov 17, Nov 17,2006  live time 83.3 days l Analysis  coherent WaveBurst pipeline  frequency band Hz

S.Klimenko, G Z, December 2006, GWDAW11 Objective of Coherent Network Analysis l Provide robust model independent detection algorithms l 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 l Detection methods should account for  variability of the detector responses as function of source coordinates  differences in the strain sensitivity of the GW detectors l Extraction of source parameters  confront measured waveforms with source models

S.Klimenko, G Z, December 2006, GWDAW11 Likelihood Likelihood for Gaussian noise with variance  2 k and GW waveform u: x k [i] – detector output, F k – antenna patterns l Find solutions by variation of L over un-known functions h +, h x Standard likelihood approach may produce unphysical solutions for h +, h x  need constraints (PRD 72, , 2005) detector response - total energy noise (null) energy detected (signal) energy

S.Klimenko, G Z, December 2006, GWDAW11 Network response matrix l Dominant Polarization Frame observables are R Z (  ) invariant l Solution for GW waveforms satisfies the equation  g – network sensitivity factor network response matrix   – network alignment factor detector frame x y z  Wave frame h+,hx x y z Rz()Rz()

S.Klimenko, G Z, December 2006, GWDAW11 Virtual Detectors Any network can be described as two virtual detectors l Use “soft constraint” on the solutions for the h x waveform.  remove un-physical solutions produced by noise  may sacrifice small fraction of GW signals but  enhance detection efficiency for the rest of sources X + (  plus X x (  cross output (DW)detector g(  )  g(  noise variancenetwork SNR g L1xH1xH2 network  g(  )  g(  noise variance

S.Klimenko, G Z, December 2006, GWDAW11 Coherent WaveBurst l Similar concept as for the incoherent WaveBurst, but use coherent detection statistic l Uses most of existing WaveBurst functionality data conditioning: wavelet transform, (rank statistics) channel 1 data conditioning: wavelet transform, (rank statistics) channel 2 data conditioning: wavelet transform, (rank statistics) channel 3,… coincidence of TF pixels generation of coincident events external event consistency final selection cuts Likelihood TF map generation of coherent events built in event consistency final selection cuts

S.Klimenko, G Z, December 2006, GWDAW11 post-production cuts simulated G-noise S5 data l For real data the pipeline output is dominated by glitches l Glitches produce responses which are typically inconsistent in the detectors. l But how to quantify inconsistent detector responses?  waveform consistency tests based on  reconstructed energy of the detector responses  network correlation coefficient

S.Klimenko, G Z, December 2006, GWDAW11 Waveform Consistency red reconstructed response black band-limited TS L1/H1 coincident glitch L1 time-shifted hrss=1.1e-21 hrss=7.6e-22 network correlation 0.3

S.Klimenko, G Z, December 2006, GWDAW11 Coincidence strategies l 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: E H1 +E H2 +E L1 >E T (same as 2L>E T  used by cWB)  double OR: E H1 +E H2 >E T && E H1 +E L1 >E T && E H2 +E L1 >E T  strict: E H1 >E T && E H2 >E T && E L1 >E T Apr 2006 “single glitches” “double glitches” rate variation by 3 orders of magnitude - total energy N i – null (noise) energy

S.Klimenko, G Z, December 2006, GWDAW11 injections glitches coherent energy & correlation l detected energy: in-coherent coherent C ij - depend on antenna patterns and variance of the detector noise x i, x j – detector output l network correlation require

S.Klimenko, G Z, December 2006, GWDAW11 Effective SNR l average SNR l effective SNR glitches: full band f >200 Hz injections 40% difference in efficiency frequency dependent threshold

S.Klimenko, G Z, December 2006, GWDAW11 S5 Rates l rates f > Hz f = Hz

S.Klimenko, G Z, December 2006, GWDAW11 Detection efficiency for bursts ratesearch S5a: 1/2.5yWB+CP S5a: 1/2.5ycWB l Use standard set of ad hoc waveforms (SG,GA,etc) to estimate pipeline sensitivity l Coherent search has comparable or better sensitivity than the incoherent search l Very low false alarm (~1/50years) is achievable in units for sgQ9 injections S5: 1/46ycWB expected sensitivity for full year of S5 data for high threshold coherent search

S.Klimenko, G Z, December 2006, GWDAW11 Adding GEO to the network Expect similar or better sensitivity for L1H1H2Geo than for L1H1H2 network if GEO noise is fairly stationary l 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 S4 S5

S.Klimenko, G Z, December 2006, GWDAW11 Network rates l Performance of LIGO-GEO network on S4 data (see Siong’s talk) l S5 rates

S.Klimenko, G Z, December 2006, GWDAW11 High threshold coherent search set thresholds to yield no events for 100xS5 data (rate ~1/50 years) ♦ - expected S5 sensitivity to sine-gaussian injections

S.Klimenko, G Z, December 2006, GWDAW11 Reconstruction of burst waveforms l If GW signal is detected, two polarizations and detector responses can be reconstructed and confronted with source models for extraction of the source parameters l 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. red reconstructed response black bandlimited TS H1/H2 coincident magnetic glitch L1 time-shifted hrss=2.4e-22 hrss=4.5e-22

S.Klimenko, G Z, December 2006, GWDAW11 Summary & Plans l 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. l 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