15
S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida Wavelets Time-Frequency analysis Coincidence Statistical approach Summary

S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

Embed Size (px)

DESCRIPTION

S.Klimenko, December 2003, GWDAW Wavelet Transform decomposition in basis {  (t)} d4d4 d3d3 d2d2 d1d1 d0d0 a a. wavelet transform tree b. wavelet transform binary tree d0d0 d1d1 d2d2 a dyadic linear time-scale(frequency) spectrograms critically sampled DWT  fx  t=0.5 LP HP

Citation preview

Page 1: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Burst detection method in wavelet domain

(WaveBurst)

S.Klimenko, G.Mitselmakher

University of Florida Wavelets Time-Frequency analysis Coincidence Statistical approach Summary

Page 2: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Wavelet basis

Daubechies

basis t bank of template waveforms 0 -mother wavelet a=2 – stationary wavelet

Fourier

wavelet - natural basis for burstsfewer functions are used for signal approximation – closer to match filter

ktaa jjjk 0

2/

notlocal

Haar localorthogonalnot smooth

local, smooth,

notorthogonal

MarrMexicanhat local

orthogonalsmooth

Page 3: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Wavelet Transform decomposition in basis {(t)}

d4

d3

d2

d1

d0

aa. wavelet transform tree b. wavelet transform binary tree

d0

d1

d2

a

dyadic linear

time-scale(frequency) spectrograms

critically sampledDWT

fxt=0.5 LP HP

Page 4: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

TF resolution

d0

d1

d2

depend on what nodes are selected for analysis dyadic – wavelet functions constant variable multi-resolution select significant pixels

searching over all nodes and “combine” them into clusters.

wavelet packet – linear combinationof wavelet functions

Page 5: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Choice of Wavelet

Wavelet “time-scale” plane

wavelet resolution: 64 Hz X 1/128 secSymlet Daubechies Biorthogonal

=1 ms

=100 ms

sg850Hz

Page 6: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

burst analysis methoddetection of excess power in wavelet domain

use waveletsflexible tiling of the TF-plane by using wavelet

packetsvariety of basis waveforms for bursts

approximation low spectral leakagewavelets in DMT, LAL, LDAS: Haar, Daubechies,

Symlet, Biorthogonal, Meyers. use rank statistics

calculated for each wavelet scale robust

use local T-F coincidence rulesworks for 2 and more interferometerscoincidence at pixel level applied before triggers

are produced

Page 7: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

“coincidence”

Analysis pipeline

bpselection of loudest (black) pixels (black pixel probability P~10% - 1.64 GN rms)

wavelet transform,data conditioning,

rank statistics

channel 1

IFO1 cluster generation

bp

wavelet transform,data conditioning

rank statistics

channel 2

IFO2 cluster generation

bp“coincidence”

wavelet transform,data conditioning

rank statistics

channel 3,…

IFO3 cluster generation

bp“coincidence”

Page 8: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Coincidence

accept

Given local occupancy P(t,f) in each channel, after coincidence the black pixel occupancy is

for example if P=10%, average occupancy after coincidence is 1%

can use various coincidence policies allows customization of the pipeline for specific burst searches.

),(),( 2 ftPftPC

reject

no pixelsor

L<threshold

Page 9: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Cluster Analysis (independent for each IFO)

Cluster Parameters

size – number of pixels in the corevolume – total number of pixelsdensity – size/volumeamplitude – maximum amplitudepower - wavelet amplitude/noise rmsenergy - power x sizeasymmetry – (#positive - #negative)/sizeconfidence – cluster confidenceneighbors – total number of neighborsfrequency - core minimal frequency [Hz]band - frequency band of the core

[Hz]time - GPS time of the core

beginningduration - core duration in time [sec]

cluster corepositive negative

cluster halo

cluster T-F plot area with high occupancy

Page 10: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Statistical Approach statistics of pixels & clusters

(triggers) parametric

Gaussian noise pixels are statistically independent

non-parametric pixels are statistically independent based on rank statistics:

iii xuRy )( – some functionu – sign function

data: {xi}: |xk1| < | xk2| < … < |xkn|rank: {Ri}: n n-1 1

example: Van der Waerden transform, RG(0,1)

Page 11: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

non-parametric pixel statistics

calculate pixel likelihood from its rank:

Derived from rank statistics non-parametric

likelihood pdf - exponential

iii x

nPRy uln

nPRixi

percentile probability

Page 12: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

statistics of filter noise (non-parametric)

non-parametric cluster likelihood

sum of k (statistically independent) pixels has gamma distribution

)()(

1

keYYpdf

kYkk

k

k

ii

k nPRY

0ln

P=10%

y

single pixel likelihood

Page 13: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

statistics of filter noise (parametric)

,2

22

pxxy

,)( yeypdf

121 px

P=10%xp=1.64

y

Gaussian noise

x: assume that detector noise is gaussian y: after black pixel selection (|x|>xp)

gaussian tails Yk: sum of k independent pixels distributed

as k

k

ik y0

Page 14: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

cluster confidence

cluster confidence: C = -ln(survival probability)

pdf(C) is exponential regardless of k.

dxexYC

kY

xkkk

1)(

1ln)(

S2 inj

non-parametric C

para

met

ric C

S2 inj

non-parametric C

para

met

ric C

Page 15: S.Klimenko, December 2003, GWDAW Burst detection method in wavelet domain (WaveBurst) S.Klimenko, G.Mitselmakher University of Florida l Wavelets l Time-Frequency

S.Klimenko, December 2003, GWDAW

Summary

•A wavelet time-frequency method for detection of un-modeled bursts of GW radiation is presented Allows different scale resolutions and wide

choice of template waveforms.Uses non-parametric statistics

robust operation with non-gaussian detector noise

simple tuning, predictable false alarm rates

Works for multiple interferometersTF coincidence at pixel levellow black pixel threshold