Download - Bursts in VIRGO
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