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

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Median based line tracker

Median Based Line Tracker

Soumya D. Mohanty

Max Planck Insitut für Gravitationsphysik

LIGO-G020034-00-Z


Lines and why remove them
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


Mblt basic motivations s d mohanty cqg 2002
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)


Mblt v s notch filters
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


Algorithm
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


Not over yet
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


Main problem
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)





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