Bursts in VIRGO

C5 run analysis

Data statisticsBurst filters

Non-stationarity investigationHardware injections

AC Clapson - LALOn behalf of the Virgo collaboration

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)

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Statistical studies: tools

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• Spectrogram

• Rayleigh monitorR ≈ 1 GaussianR << 1 coherentR >> 1 non-coherent (fast fluctuations)

Plot |1-R|

• Frequency power 2 testOn log-spectrogram of whitened data, confidence level of non-stationarity.Event = confidence > 99.9%

• Frequency band spectral flatnessComputed after whitening.ξ ~1 for flat spectrum.Plot 1-ξ

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Statistical studies: overview

2 test “Rayleighogram”

Frequency (Hz)

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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.

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Statistical studies: time view

Overall limited fluctuations.

Small trend in PSD.

No systematic coincidence in peak

location.

Information extraction?

Gaussian data reference

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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.

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Burst search methods II

Mean Filter Peak Correlator

EGC Power Filter

Type Time domain Correlator

(Gaussian)

Correlator / TF

(Exp. Gaussian)

Time-Frequency

Pre-processing

Static whitening.Mean and sigma normalized.

Static whitening.

Mean and sigma normalized.

Adaptivity Normalization of 300 s chunks.

PSD update every ~13 s

PSD update every ~13 s

Normalization of ~8 s chunks.

Methods involved in C5 investigations

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Burst search summary

Using 40 highest energy events for each method:Single detection: 47, double 18, triple 11, quadruple 11.

Dots for all eventsOther symbols differentiate methods.

C5 “quiet”Segment.

Single detection Double detection

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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?

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What do we trig on?

Seen by all methods

Highest SNRevent in segment.

Lower energy events hard tofind visually.

Veto candidate?

In-phase channel

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

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…yet invisible in acoustic and magnetometers channels from central building.

Veto investigation II13

Non-stationarity hint

Average SNR evolutionTrigger count evolution

Computed quantities– Trigger count

– Averaged SNR

(over 930s periods)

X 2

X 2

X 2

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

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Over 5h quiet period,MF trigger density increaseswith time…

… 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.

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MF principle – Multiple window sizes– Whitening (+normalization)– Event clusterization

11 Nk

kii

MFk x

Ny

Hardware injections searches

• Injections: numerical core collapse, Sine-Gaussian, NS-NS• Burst filters:

MF and PF.• Noise level issue.• SNR accuracy?

Low noise period, DFMa1b2g1

MF PF

FA (Hz) 0.09 0.03

Efficiency @ SNR 7 (%) 21 18

Efficiency @ SNR 14 (%) 97 98

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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

“Jump”

“Standard” noise

ALF on M1

Output ~ 4000

Output~40

Jump investigation

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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.

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Complements

B1 photodiode

99.6 %

BS

0.4 %

B1p

Data in WPR_B1p_DC

d1

d250 %

50 %

B1s

50 %d1

d2

50 %

Data in WPR_B1s_DC

OMC

B1

d8

d6

50 %

50 %

Data in WPR_B1_DC

Faraday

96 %

Signal construction

Statistical studies: encore

Lowest frequencies most affected by variability.

Flatness estimator

MF triggers : details

Burst filter performances

ROC for MF ROC for PF

SNR 10

SNR 8

SNR 7

SNR 5