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Burst detection in time-frequency space

Burst detection in time-frequency space. Unfolding signal structure. Starting point unbiased toward specific signals. Aim at timing and frequency band accuracy. Nb: all results based on Virgo-type simulated data (LIGO-VIRGO joint group). AC Clapson. GWDAW 9, Annecy, December 2004.

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Burst detection in time-frequency space

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  1. Burst detection in time-frequency space • Unfolding signal structure. • Starting point unbiased toward specific signals. • Aim at timing and frequency band accuracy. Nb: all results based on Virgo-type simulated data (LIGO-VIRGO joint group). AC Clapson GWDAW 9, Annecy, December 2004

  2. S transform • Gaussian shaped complex exponential template. (applied in frequency domain) • Resolution defined by Gaussian width and centre • Linear centre frequency spacing. • Width increases with frequency. • Colored spectrum issue • Spectrum reproduced in TF map. • Noise / signal discrimination based on excess energy: adaptive criterion. • Spectral lines issue • Line coupling with successive templates gives wide frequency profile. • Locally, line energy much larger than signal.

  3. Initial analysis chain • Preprocessing • Line removal (Kalman filters). • High pass filter. • Segment edges Hann-shaped. • Transform • 215 bins long, exclude edges from map. • Output power. • Band 30 – 500 Hz. • Normalize mean and standard deviation (for each frequency). • Event extraction • Bin energy cut. • Clusterize bins. • Cluster energy cut.

  4. Results • Satisfying efficiency for sine Gaussians and short Gaussian peaks. • Energy well localized in TF map (compacity and frequency band) • Lower detectability for long Gaussian peak and DFM. • Energy reduction by preprocessing. • Spread out energy: secondary clusters. • Map frequency cut : high frequency component missed.

  5. Signals First improvement Map size increase: • Statistics more accurate. • Better frequency separation by template.

  6. (Preliminary results) Improvement under study • Non-linear list of template frequency centres. • Gaussian templates overlap set by equal s rule. • Reduces template number (1/3 here). • Requires energy renormalization (restore time-frequency atom integral)

  7. Summary • Core transform promising. • Compact signals efficiently extracted. • Complex signals require finer treatment. • Noise spectral features rapidly detrimental. • Series of modifications under study. • Alternative S transform • Implement “à la Welch” windowing (discard high pass). • Use non-linear frequency spacing (constant s spacing). • Avoid self-normalization. • Elaborate clustering / thresholding • Time coincidence before cluster energy cut. • Additional cluster parameter cuts (noise discrimination). AC Clapson

  8. Measure Prediction Correction Model Kalman filters for line removal • Relies on model of the feature to be extracted. • Discrete time first order recursive model => fast. • Optimal filter in Wiener sense (minimal reconstruction error) • Model mismatch detection and correction required. • Model: • Thermally excited violin modes • Viscous damping oscillator equation • Parameters : m, Q, T, w0 , x0, x’0

  9. Template spacing • Balance centre frequency distance / template overlap. • fn+1 = fn + df • fn+1 = fn * sf

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