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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
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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
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
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
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
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
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
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”
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
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
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)
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
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
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
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
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