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Frédérique MARION. Status of inspiral search in C6 and C7 Virgo data. for the Collaboration GWDAW 2005. C6 & C7 runs. Optimally oriented [1.4,1.4] M  (SNR=8). C6 14 days 86% duty cycle (in science mode)

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For the collaboration gwdaw 2005

Frédérique MARION

Status of inspiral search in C6 and C7 Virgo data

for the Collaboration

GWDAW 2005


C6 c7 runs

C6 & C7 runs

Optimally oriented [1.4,1.4] M (SNR=8)

  • C6

    • 14 days

    • 86% duty cycle

      (in science mode)

    • Factor 2 variation in the horizon over the course of the run

  • C7

    • 5 days

    • 65% duty cycle

    • Detector sensitivity also not stationary


Inspiral online analysis

Inspiral online analysis

  • Both pipelines, Merlino and MBTA, ran online during C6 and C7, getting data from online h-reconstruction

    • Monitoring information provided

      • Lists of loudest events

      • Plots for monitoring web pages

MBTA

Hardwareinjections


C6 hardware injections

C6 hardware injections

  • 56 inspiral hardware injections

    • 4 different periods, [1.4,1.4] M, SNR ranging from ~15 to 25

  • All detected

    • Check timing and mass estimation accuracy

    • Check SNR recovery

      ~ 10% loss might be due to sensitivity non-stationarity

MBTA


C7 hardware injections

C7 hardware injections

  • 2 periods of inspiral injections

    • [1.32 , 1.36] M, SNR ~ 20

  • Detected with good accuracy

    • Issue with some injections having a bad 2

      • under investigation

Merlino


Non stationarities c6

± 10% variations on short time scales(~15 min)

Smaller variations still present

Non-stationarities: C6

  • Evolution in time of the frequency at which half of the SNR is integrated

     Change in the shape of the PSD


Non stationarities c7

Same kind of variations observed in C7

Non-stationarities: C7

  • Variations of the PSD on short time scales not obvious to follow with adaptive mechanisms


C6 analysis quality cuts i

Dark fringe spectrum

C6 analysis: quality cuts (I)

  • C6 first six days

    • Horizon very unstable

    • “Bump” going up and down in 100-300 Hz region of sensitivity curve (stray beam)

    • Leave this first part of the run aside

  • Focus on remaining 8 days


C6 analysis quality cuts ii

C6 analysis: quality cuts (II)

  • Skip first five minutes after relock

  • Use auxiliary channels to identify obvious sources of high SNR triggers, e.g.

    • Saturations in frequency stabilization loop

    • Glitches on actuator coil drivers


C6 analysis snr distribution

C6 analysis: SNR distribution

MBTA

Inspiral injections

  • [0.9 - 3] M analysis

    • ~ 170 hours of data

  • Very loud events eliminated by basic quality cuts

  • Tail of distribution still extends to high values

    • Even strong, the hardware injections (in red) do not dominate the noise

Burst injections


C7 analysis

MBTA

Merlino

A relatively quiet 10 hours long period

C7 analysis

  • [0.9 - 3] M analysis

    • ~ 80 hours of data

  • C7 SNR distribution slightly better than C6


C7 software injections

C7 software injections

  • Systematic studies (efficiency, 2 test) with Merlino on software injections performed during C7 quiet period

     see poster by L.Bosi


C6 a posteriori vetoes

noise event

hardware injection

noise event

C6: a posteriori vetoes

  • SNR distribution between low and high frequency bands

    • 22 bands

  • Shawhan-Ochsner veto

    • Look at output of matched filter in time domain


C6 snr with vetoes

22 bands < 100

Samples above threshold < 30

22 bands< 100

Samples above threshold < 30

C6: SNR with vetoes

  • Bulk of distribution unchanged

  • High SNR tail definitely improved

    • Hardware injections almost stand out


C7 vetoes

C7: vetoes

  • Behavior similar to C6


C7 snr with vetoes

C7: SNR with vetoes

22 bands < 40

  • Loudest surviving event has SNR < 20

Samples above threshold < 30

22 bands< 40

Samples above threshold < 30


Conclusion

Conclusion

  • C6 and C7 data have extra-galactic sensitivity to neutron star inspirals

  • Not science quality data

    • Detector behavior glitchy and not stationary

  • Preliminary analysis

    • Two pipelines operational for online and offline analysis

    • Hardware injections, quality cuts, a posteriori vetoes

  • Pursue investigations

    • Learn as much as we can from those data in order to get prepared for the science data to come next year


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