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Bursts in VIRGO

Bursts in VIRGO. C5 run analysis Data statistics Burst filters Non-stationarity investigation Hardware injections. AC Clapson - LAL On behalf of the Virgo collaboration. 2. Interest of VIRGO C5 run. Stable recombined (no PR) optical configuration Duration and quality

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Bursts in VIRGO

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  1. Bursts in VIRGO C5 run analysis Data statistics Burst filters Non-stationarity investigation Hardware injections AC Clapson - LAL On behalf of the Virgo collaboration

  2. 2 Interest of VIRGO C5 run • Stable recombined (no PR) optical configuration • Duration and quality • Science mode for long stretches • Hardware injections • Important transition from simulated Gaussian noise. • Focus on • ‘Quiet’ data segment (~ 5h). • Dark fringe signal (DC, in-phase, quadrature)

  3. 3 Statistical studies: tools • Spectrogram • Rayleigh monitor R ≈ 1 Gaussian R << 1 coherent R >> 1 non-coherent (fast fluctuations) Plot |1-R| • Frequency power c2 test On log-spectrogram of whitened data, confidence level of non-stationarity. Event = confidence > 99.9% • Frequency band spectral flatness Computed after whitening. ξ ~1 for flat spectrum. Plot 1-ξ

  4. 4 Statistical studies: overview Frequency (Hz) c2 test “Rayleighogram”

  5. 5 Statistical studies: frequency view • Approximately Gaussian • Specific line behaviours • non-Gaussian • frequency modulation? • Most variability • 0 - 350 Hz • 3000 - 4000 Hz • 6000 - 7000 Hz • Non-equivalent tools. • Frequency range • Sensitivity to • local features.

  6. 6 Statistical studies: time view Overall limited fluctuations. Small trend in PSD. No systematic coincidence in peak location. Information extraction? Gaussian data reference

  7. 7 Burst search methods • Time domain • Mean Filter (MF) • Alternative Linear Fit Filter (ALF) • Correlators • Gaussian (PC) • complex Exponential Gaussian (EGC) • Sine Gaussian –tiling based- • TF domain • Power Filter (PF) • Fourier Domain Adaptive Wiener Filter (FDAWF) • S Transform (involved methods) (not used here) NB: Not all filters produce SNR consistent outputs.

  8. 8 Burst search methods II Methods involved in C5 investigations

  9. 9 Burst search summary Single detection Double detection C5 “quiet” Segment. Dots for all events Other symbols differentiate methods. Using 40 highest energy events for each method: Single detection: 47, double 18, triple 11, quadruple 11.

  10. 10 Burst search summary II • Many non-coincident triggers. • Known filter-dependent coupling to waveforms. • Time varying outputs. • Partial correlation with statistical overview. • Focus on different time scales. • Complementary approaches. • Quality flag relevance?

  11. Seen by all methods 11 What do we trig on? In-phase channel Highest SNR event in segment. Lower energy events hard to find visually. Veto candidate?

  12. 12 Veto investigation with MF Highest SNR glitch in stretch : Weak on composite dark port and demodulated signals, … but clear in photodiode channels…

  13. 13 Veto investigation II …yet invisible in acoustic and magnetometers channels from central building.

  14. X 2 X 2 X 2 14 Non-stationarity hint Over 5h quiet period, MF trigger density increases with time… Trigger count evolution Average SNR evolution Computed quantities • Trigger count • Averaged SNR (over 930s periods) • Clear increase of trigger density in the 3 channels. • (consistent with PSD trend) • <SNR> constant on demodulated signal, increasing on DC. Quiet period: 5h Quiet period: 5h

  15. MF principle • Multiple window sizes • Whitening (+normalization) • Event clusterization 15 … and investigations • Gaussian stationary models check • Compare to simulation data • Auto-regressive model derived from data PSD. • Trend not reproduced in Gaussian data. • Change whitening coefficients • Training set either at beginning or end of segment. • Trigger count variation but trend maintained. • Trend not caused by whitening errors. • Trigger typology • Observed trend is specific of short windows (< 3.5 ms) • Two local fluctuation periods found for larger windows. • Similar behaviour on all three dark port channels. • Throughout exploitation of method’s results. • Importance of adaptivity time-scale. • Local fluctuations issue.

  16. 16 Hardware injections searches • Injections: numerical core collapse, Sine-Gaussian, NS-NS • Burst filters: MF and PF. • Noise level issue. • SNR accuracy?

  17. Jump investigation ALF on M1 “Jump” Output ~ 4000 “Standard” noise Output~40 17 Last word • Relatively short stretch • Unique observations • Prototype study • Involve many complementary tools • Investigation of deviation from stationarity. • Group activity • Commissioning “Mini-Runs” • LIGO-Virgo joint work

  18. 18 Conclusion: burst analysis in VIRGO • Large toolbox for • Data characterization • Burst search. • C5 most extensive analysis so far. • Expectations for C6 • Recycled ITF • Longer stretches of data. • Topics to develop • Multi-channel coincidence • Integration of methods in synthetic picture.

  19. Complements

  20. BS Signal construction OMC B1 photodiode Data in WPR_B1_DC B1 d6 50 % 99.6 % d8 Faraday 50 % 96 % 0.4 % d2 B1s Data in WPR_B1p_DC 50 % d2 50 % d1 50 % 50 % B1p d1 Data in WPR_B1s_DC

  21. Statistical studies: encore Flatness estimator Lowest frequencies most affected by variability.

  22. MF triggers : details

  23. Burst filter performances ROC for PF ROC for MF SNR 10 SNR 8 SNR 7 SNR 5

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