Soma Mukherjee GWDAW8, Milwaukee, December'03 Interferometric Data Modeling: Issues in realistic data generation. Soma Mukherjee CGWA Dept. of Physics.

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Soma Mukherjee GWDAW8, Milwaukee, December'03 Interferometric Data Modeling: Issues in realistic data generation. Soma Mukherjee CGWA Dept. of Physics and Astronomy University of Texas Brownsville LIGO Tech Doc #LIGO-G Z

Soma Mukherjee GWDAW8, Milwaukee, December'03 Why do we need a model ? Astrophysical searches estimate efficiency from playground data. Externally triggered search (Gamma Ray Bursts with GW) – how representative is the ’off-source’ segment of the ‘on-source’ one ?

Soma Mukherjee GWDAW8, Milwaukee, December'03 Data Modeling: Basis Interferometric data has three components : lines, transients and noise floor. As a first approximation, the three components are independent and appear additively. Physically different sources for each Basic idea is to split a channel into these components with mutual exclusion Classify Transients, fit ARMA models to line amplitude and phase modulation, ARMA models for noise floor rms

Soma Mukherjee GWDAW8, Milwaukee, December'03 Issue I :Slowly drifting noise floor Data from present generation of interferometers is non-stationary. Results obtained by running MNFT 1 on a stretch of LIGO S2 data.[ 1 Mukherjee, CQG, 2003] Low pass and resample Estimate spectral Noise floor with Running Median Whiten data using FIR whitening Filter. Clean lines and Highpass Compute Running Median of the Squared timeseries Set threshold

Soma Mukherjee GWDAW8, Milwaukee, December'03 Issue II : Modeling ALL lines MBLT 1 : Non-parametric Line estimation. [ 1 Mohanty CQG, 2001 ]

Soma Mukherjee GWDAW8, Milwaukee, December'03 Non-parametric change point detector. KSCD : Kolmogorv-Smirnov test based Change point Detector Mohanty, GWDAW (2002); PSDCD, Mohanty, PRD (2000); Issue III : Transient Classification 12 top wavelet coefficients of data surrounding each KSCD trigger. Visualized using GGobi. (Preliminary). Mukherjee, 2003, Amaldi, Pisa

Soma Mukherjee GWDAW8, Milwaukee, December'03 MNFT outline: Algorithm: 1. Lowpass and resample given timeseries x(k). 2. Construct FIR filter that whitens the noise floor. Resulting timeseries : w(k) 3. Remove lines using notch filter. Cleaned timeseries : c(k) 4. Track variation in second moment of c(k) using Running Median and apply smoothing (SRM). 5. Obtain significance levels of the sampling distribution via Monte Carlo simulations.

Soma Mukherjee GWDAW8, Milwaukee, December'03 Model Noise Generation Estimate lines using MBLT, ARMA model amplitude and phase, add reconstructed lines to synthetic data. Add transients. Use MNFT to Compute smoothed Running Median Fit ARMA to the SRM Model Noise Floor (low order ARMA).

Soma Mukherjee GWDAW8, Milwaukee, December'03 ARMA (p,q) A(T) y(t) = C(T) e(t) Y(t) : Output e(t) : White noise C(T)/A(T) : Transfer function T: Time shift operator A and C : Polynomials

Soma Mukherjee GWDAW8, Milwaukee, December'03 How faithful is the model ? Apply statistical tests of hypothesis Kolmogorov-Smirnov Akaike Information criterion (AIC) I akaike (p,q) = ln  2 p,q + 2 (p+q)/N

Soma Mukherjee GWDAW8, Milwaukee, December'03 Result I : Noise floor model – ARMA (12,7)

Soma Mukherjee GWDAW8, Milwaukee, December'03 Result II : Line Amplitude - ARMA (27,11)

Soma Mukherjee GWDAW8, Milwaukee, December'03 Line model : Phase – ARMA (5,2) ARMA (5,2)

Soma Mukherjee GWDAW8, Milwaukee, December'03 Plans Applicable to band limited data. Use as an ‘infinite’ playground for astrophysical searches. Gives a handle on non-stationarity and hence testing the robustness of the search algorithm. Allows us to do ‘controlled tests’. Signal injection and efficiency estimation