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Median Based Line Tracker

This document outlines the significance of removing non-fundamental noise sources, particularly lines, in gravitational wave detection. It discusses the impact of lines on transient detection, false alarm rates, and noise floor efficiency. The Median-Based Line Tracker (MBLT) algorithm is introduced as a robust method for line removal. It highlights the advantages of using a running median over traditional filters in maintaining signal integrity amid noise transients. The efficiency of MBLT in handling multiple lines with a focus on computational feasibility is also addressed.

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Median Based Line Tracker

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  1. Median Based Line Tracker Soumya D. Mohanty Max Planck Insitut für Gravitationsphysik LIGO-G020034-00-Z

  2. Lines and why remove them • Lines are not fundamental noise sources (except violin mode thermal noise) • Effects of lines: examples • Higher false alarm rates for transient tests • Reduction in efficiency of noise floor non-stationarity tracking. LIGO E7 run

  3. MBLT: Basic Motivations(S.D.Mohanty, CQG, 2002) • Need to have a model independent line removal method • There are usually more lines than models • Violin modes (Kalman filter) • Power Lines (CLRT) • Cross-channel regression requires that the predictor channel have • a high SNR for the line to be removed. • a stationary noise background (and no strong transients)

  4. MBLT v/s Notch Filters • Notch filters are model independent • Subtracting away line estimate found from notch filters takes away power from transients • If there are a lot of lines, the loss of collective bandwidth can be important • MBLT was created to be a notch filter that is more robust against transients

  5. Algorithm • Heterodyne data at every line carrier frequency • Make a smoothed estimate of the two noisy quadratures • Modulate a carrier with the smoothed estimates • Using a running mean for smoothing is equivalent to a notch filter Use the Running Median instead : rejects outliers unlike the mean

  6. Not over yet ... • The main contamination comes not from background noise but from nearby lines! • Remove all other lines except one • Estimate that line again • Do the same for all lines • Iterate

  7. Main problem • Finding the median of a set involves sorting which is computationally expensive • But getting a running median is much easier since most of the points are already sorted • C code for a running median written (Mohanty 2001) • Reduces O(Nm^2) tp O(Nm^0.5)

  8. Cleaned data

  9. Effect of transient

  10. Cleaned data; Periodogram

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