s.klimenko, g060247-00-z, december 2006, gwdaw11 coherent detection and reconstruction of burst...

S.Klimenko, G060247-00-Z, December 2006, GWDAW11

Coherent detection and reconstruction of burst events in S5 data

S.Klimenko, University of Floridafor the LIGO scientific collaboration

S5 data coherent network analysis coherent WaveBurst S5 results (all results are preliminary) Summary


S5 run 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


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 coordinatesdifferences in the strain sensitivity of the GW detectors

Extraction of source parametersconfront measured waveforms with source models


Likelihood Likelihood for Gaussian noise with variance 2

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)

i k

kkkk

iixixL 222 ][][

21

xkxkk FhFh detector response -

NEL 2total

energynoise (null)

energydetected (signal)

energy


Network response matrix Dominant Polarization Frame observables are RZ() invariant

Solution for GW waveforms satisfies the equation

g – network sensitivity factor network response matrix

– network alignment factor

hh

XX

hh

F

F

Fix

Fix

kk

k

kk

k

k kk

k

k kk

k

0

01g

0

0

21

][

][

2

2

2

2

2

2

detectorframe x

y

z

Wave frameh+,hxx y

zRz()

02

kk

DPFkDPFk FF


Virtual Detectors 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

X+(plusXx(cross

output (DW)detectorg()g(

noise variance network SNR2hg2hg

g L1xH1xH2 network

g()g(

noise variance


Coherent WaveBurst

Similar concept as for the incoherent WaveBurst, but use coherent detection statistic

Uses most of existing WaveBurst functionality

data conditioning:wavelet transform,

(rank statistics)

channel 1


(rank statistics)

channel 2


(rank statistics)

channel 3,…

coincidence of TF pixels

generation of coincident events

external event consistencyfinal selection cuts

Likelihood TF mapgeneration of coherent

events

built in event consistencyfinal selection cuts


post-production cutssimulated 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


Waveform Consistency

redreconstructedresponse

blackband-limited TS

L1/H1 coincident glitch

L1 time-shifted

hrss=1.1e-21

hrss=7.6e-22

hrss=7.6e-22

network correlation 0.3


Coincidence strategies 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

iii NxE 2

Apr 2006

“single glitches”

“double glitches”rate variationby 3 orders

of magnitude

<xi2> - total energy

Ni – null (noise) energy


injectionsglitches

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

coherentull

coherentnet EN

EC

jijiji

ijji EECxxL ,

2

0.65netC


Effective SNR

average SNR

effective SNR

3/1211 HHL

netCeff

glitches: full bandf >200 Hz

injections

40% difference in efficiency

frequency dependent threshold


S5 Rates rates

f>200-2048Hz

f=64-2048 Hz


Detection efficiency for bursts

rate search 70 100 153 235 361 553 849 1053S5a: 1/2.5y WB+CP 40.3 11.6 6.2 6.6 10.6 12.0 18.7 24.4S5a: 1/2.5y cWB 28.5 10.3 6.0 5.6 9.6 10.7 16.9 21.9

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

hrss@50% in units 10-22 for sgQ9 injections

S5: 1/46y cWB 25.3 9.5 6.1 5.1 8.7 9.8 15.2 20.0expected sensitivity for full year of S5 data for high threshold coherent search


Adding GEO to the network

112111 && GLHLHL

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

S4 S5

22

22

detected ,y sensitivitnetwork rssk k

kk ghSNRFFg


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


High threshold coherent search

set thresholds to yield no events for 100xS5 data (rate ~1/50 years)♦- expected S5 sensitivity to sine-gaussian injections


Reconstruction of burst waveforms

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.

redreconstructedresponseblack

bandlimited TS

H1/H2 coincident magnetic glitch

L1 time-shifted

hrss=2.4e-22

hrss=4.5e-22

hrss=4.5e-22


Summary & Plans

coherent WaveBurst pipelinegenerate coherent triggers for one year of S5 datarobust 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 ratesanalyze zero lag triggers produce final result

s.klimenko, g060247-00-z, december 2006, gwdaw11 coherent detection and reconstruction of burst...

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