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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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