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

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Presentation transcript:

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

Gabriele Vajente, 12 th ILIAS WG1 meeting2 Summary HACR algorithm Implementation at Virgo Some results from WSR9

Gabriele Vajente, 12 th ILIAS WG1 meeting3 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

Gabriele Vajente, 12 th ILIAS WG1 meeting4 How HACR works Compute short FFTs (50 ms) of a signal without averages Frequency resolution about 40 Hz Create a time – frequency map of the signal Simulated data, white noise plus sine-gaussian Zoom around one transient

Gabriele Vajente, 12 th ILIAS WG1 meeting5 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

Gabriele Vajente, 12 th ILIAS WG1 meeting6 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)

Gabriele Vajente, 12 th ILIAS WG1 meeting7 How HACR works Trigger if the significance is above a given threshold T HIGH Build up a cluster by searching neighboring points with significance above a lower threshold T LOW 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 Points above THIGH Points above TLOW CLUSTER

Gabriele Vajente, 12 th ILIAS WG1 meeting8 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 channels took 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

Gabriele Vajente, 12 th ILIAS WG1 meeting9 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

Gabriele Vajente, 12 th ILIAS WG1 meeting10 Time – Frequency – SNR map

Gabriele Vajente, 12 th ILIAS WG1 meeting11 Distributions

Gabriele Vajente, 12 th ILIAS WG1 meeting12 Correlograms WSR7 WSR9 During WSR7 strong 600mHz excitation of BS transversal motion

Gabriele Vajente, 12 th ILIAS WG1 meeting13 Coincidences phase - quadrature Coincidence between phase and quadrature of dark fringe signal

Gabriele Vajente, 12 th ILIAS WG1 meeting14 Coicidences both frequency and time 20 % of all glitches are coincident both in time and frequency dt < 0.1s df < 100 Hz

Gabriele Vajente, 12 th ILIAS WG1 meeting15 Coincidences both time and frequency

Gabriele Vajente, 12 th ILIAS WG1 meeting16 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?