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Noise Floor Non-stationarity Monitor

Noise Floor Non-stationarity Monitor. Roberto Grosso*, Soma Mukherjee + + University of Texas at Brownsville * University of Nuernburg, Germany. Detector Characterization Telecon April 22 ‘05. What does this monitor do ?.

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Noise Floor Non-stationarity Monitor

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  1. Noise Floor Non-stationarity Monitor Roberto Grosso*, Soma Mukherjee+ + University of Texas at Brownsville * University of Nuernburg, Germany Detector Characterization Telecon April 22 ‘05

  2. What does this monitor do ? The monitor tracks variation or drifts (I.e. changes in the statistical Characteristics) in the noise floor (i.e. component of interferometric data that is left behind after subtraction of lines and sharp transients). This monitor is based on the ‘Median based noise floor tracker’ algorithm that has been presented earlier in LSC meetings. A paper has been published in CQG (ref : Median based noise floor tracker (MNFT) : robust estimation of noise floor drifts in interferometric data, CQG, 20, 925-936 2003).

  3. Method : • Bandpass and resample given timeseries x(k). • Construct FIR filter than whitens the noise floor. Resulting timeseries : w(k) • Remove lines using notch filter. Cleaned timeseries : c(k) • Track variation in second moment of c(k) using Running Median*. • Obtain significance levels of the sampling distribution via Monte Carlo simulations. *Mohanty S.D., 2002, CQG, 19, 1513-1520

  4. Low pass and resample Estimate spectral noise floor using Running Median Design FIR Whitening filter. Whiten data. Thresholds set by Simulation. Compute Running Median of the squared timeseries. Clean lines. Highpass.

  5. Comments on the Code • The code • was developed using a precompiled linux version of the DMT. • dmt/rev_2.10/ • /CERN/root_4.00-08/ • uses frame v6r16 and fftw-3.0.1 • uses the classes Tseries and Interval for managing timeseries and time. • is based on the DatEnv class, I.e. the class NoiseFloor Monitor is • derived from the class DatEnv. • The output is now written into ascii files, but we are implementing the • Graphics capabilities of DMT/root to plot the results online.

  6. Output :

  7. Future improvements: The monitor will have several improved features by next two weeks, e.g. online display, band limited monitoring etc. We intend to have whole S4 data is analyzed and results available by June. A web page will be set up and maintained and results will be regularly reported to the Det Char group.

  8. Figure of Merit SNR for any signal (say, a 1.4 1.4 solar mass binary inspiral, or sine- Gaussian etc. ) using matched filter can be calculated first using a Gaussian stationary noise. The output of the monitor can be accurately modeled using ARMA models (ref : Mukherjee, S., Interferometric data modeling : Issues in realistic data generation, CQG, 21, 1783-1794, 2004). SNR for the same signal can now be calculated using this a sufficiently long realization of the modeled noise. The deviation ratio can be plotted at each instant of time as a FOM to Indicate the sensitivity of the searches.

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