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HACR at Virgo: implementation and results

HACR at Virgo: implementation and results. Gabriele Vajente 12 th ILIAS WG1 meeting Geneva, March 29 th -30 th 2007. Summary. HACR algorithm Implementation at Virgo Some results from WSR9. HACR algorithm. Based on HACR developed at GEO60 Search for transients in time-frequency domain

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HACR at Virgo: implementation and results

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  1. HACR at Virgo: implementation and results Gabriele Vajente 12th ILIAS WG1 meeting Geneva, March 29th -30th 2007

  2. Summary • HACR algorithm • Implementation at Virgo • Some results from WSR9

  3. HACR algorithm • Based on HACR developed at GEO60 • Search for transients in time-frequency domain • Times and frequencies with an excess of power with respect to the mean

  4. How HACR works Simulated data, white noise plus sine-gaussian • Compute short FFTs (50 ms) of a signal without averages • Frequency resolution about 40 Hz • Create a time – frequency map of the signal Zoom around one transient

  5. How HACR works • Compute an averaged spectrum over time • Using a decay average with time constant of about 10 s • Compute also the standard deviation over time for each frequency

  6. How HACR works • Compute the deviation of the point from the mean • This gives an indication of how much each point deviates from the mean statistics of the spectrum(significance of the bin)

  7. Trigger if the significance is above a given threshold THIGH Build up a cluster by searching neighboring points with significance above a lower threshold TLOW For each cluster compute Mean time and frequency (weighted average with power) Time and frequency width Maximum and mean significance SNR as square root of power in the cluster divided by power in the mean spectrum Number of points How HACR works CLUSTER Points above THIGHPoints above TLOW

  8. Implementation at Virgo • Program written in C using FFTW and Virgo frame interface • Running online since a couple of weeks • Some performances • 10 hours of data, analyzing 11 channelstook 3.3 hours • Analysis of one frame, one 20kHz channel, estimated with profiler 14 ms of machine time • Limited mainly by data access • Output sent to MySQL database • Also: number of cluster per second and max snr for each second sent to main data stream

  9. Report generator • Simple script written in Perl and Octave • Query the database, perform basics analysis and produce plots • Time, frequency, SNR distribution • Time – freuency – SNR maps • Correlograms • Coincidences between pairs of channels • Examples at http://wwwcascina.virgo.infn.it/MonitoringWeb/HACR/

  10. Time – Frequency – SNR map

  11. Distributions

  12. Correlograms WSR9 WSR7 • During WSR7 strong 600mHz excitation of BS transversal motion

  13. Coincidences phase - quadrature • Coincidence between phase and quadrature of dark fringe signal

  14. Coicidences both frequency and time 20 % of all glitches are coincident both in time and frequency dt < 0.1s df < 100 Hz

  15. Coincidences both time and frequency

  16. Conclusions • HACR is working well at Virgo • Still some tuning needed for the reports • Soon the report generator will run automatically (once every 8 hours) • HACR can give lots of information on the quality of data • Vetoes?

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