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WaveBurst upgrade for S3 analysis S.Klimenko, I.Yakushin University of Florida Motivation

WaveBurst upgrade for S3 analysis S.Klimenko, I.Yakushin University of Florida Motivation Multi-resolution analysis Parameter space Summary. Implementation of “original” WB algorithm add analysis at multiple TF resolutions more flexible time shift analysis

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WaveBurst upgrade for S3 analysis S.Klimenko, I.Yakushin University of Florida Motivation

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  1. WaveBurst upgrade for S3 analysis S.Klimenko, I.Yakushin University of Florida Motivation Multi-resolution analysis Parameter space Summary

  2. Implementation of “original” WB algorithm add analysis at multiple TF resolutions more flexible time shift analysis single and multiple detector options use different analysis environment: DMT+Condor make development cycle shorter simplify debugging, validation and testing reduce data processing time motivation

  3. linear dyadic d0 d0 d1 d2 d1 d3 d2 d4 a a a. wavelet transform tree b. wavelet transform binary tree Wavelet scalograms decomposition in basis {Yi(t)} critically sampled DWT DfxDt=0.5 LP HP time-scale(frequency) spectrograms

  4. t=100 ms t=1 ms Wavelet Resolution representation of SG 850Hz with Symlet wavelet t=10 ms resolution 64 Hz X 1/128 sec optimal

  5. Optimal Resolution run analysis in a range of TF resolutions (rotation in larger space) t=10 ms 64 Hz X 1/128 sec optimal t=1 ms 256 Hz X 1/512 sec t=1 ms t=100 ms 8 Hz X 1/16 sec optimal optimal

  6. cover range of resolutions by selecting nodes from wavelet tree. select pixels with maximum excess power Df/2 Df 2Dt Dt WaveBurst multi-resolution analysis wavelet tree

  7. Signals with large TF volume ? for example windowed white noise. need multi-detector pipeline coherent excess power (model independent, implemented in WB) similarity of waveforms (r-statistics, can be model dependent) Coverage of the parameter space

  8. “LIGO cheese”: fc, Df, Dt (Lazzarini, Sutton) Two-dimensional space: fc, V=Df x Dt A time series sample has volume rs/2 x 1/rs = 1/2 (rs sampling rate) sources f, Hz LIGO band noise 1 10 100 V signal/noise parameter space

  9. “ LIGO noise parameter space S2 S3pg White noise

  10. Burst signal with SNR 25: P=10% P=31% ROC matched filter V=2 V=5 V=20

  11. S2 rates

  12. See Igor’s talk S2 efficiency for SG

  13. WB version v3 S3 vs S2

  14. WB version v5 S3 vs S2

  15. WaveBurst upgrade Implemented multiple TF resolutions Sensitivity improvement 20%-100% single and multiple detector options S3 playground studies (both v4 & v5 versions) sensitivity & rates noise non-stationarity resolve S3 analysis issues before next LSC Preparations for S4 Summary & Plans

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