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WaveMon and Burst FOMs Sergey Klimenko University of Florida WaveMon WaveMon FOMs Summary & plans

WaveMon and Burst FOMs Sergey Klimenko University of Florida WaveMon WaveMon FOMs Summary & plans. detect glitches in slave channels coincident with master channel, which could be AS_Q channel  inter-channel correlations

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WaveMon and Burst FOMs Sergey Klimenko University of Florida WaveMon WaveMon FOMs Summary & plans

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  1. WaveMon and Burst FOMs Sergey Klimenko University of Florida WaveMon WaveMon FOMs Summary & plans

  2. detect glitches in slave channels coincident with master channel, which could be AS_Q channel  inter-channel correlations Slave triggers can be used as an efficient veto in the burst analysis (see Ken’s and Laura’s talks) master channel slave channel,… wavelet transform, data conditioning rank statistics wavelet transform, data conditioning, rank statistics bp bp “coincidence” 10% slave event generation WaveMon • DMT tool to monitor glitches • Wavelet domain: complete time-frequency cluster analysis • Same algorithms as for WaveBurst • Fast: can monitor up to 60 channels in the frequency band up to 4 kHz.

  3. S3 rates for L1:LSC-MICH_CTRL random coincidence rate at 30 sec time lag “permanent problem” rate at 0 sec time lag • good FOM ? – not really • however, tells us that MICH_CTRL is efficient veto channel.

  4. S3 rates for L1:LSC-REFL_I (FOM1) random coincidence rate at 30 sec time lag rate at 0 sec time lag • good FOM ? – yes!

  5. S3 rates for L1:LSC-REFL_Q random coincidence rate at 30 sec time lag rate at 0 sec time lag

  6. Purpose display the performance of LIGO detectors to identify (new) problems during data taking runs Sensitivity: range or strain (preferably for astro-motivated sources) glitch rates noise outliers (non-stationarity & non-Gaussianity) latency Short (few minutes) to be useful in control room Long (hours-days) - could be useful for analysis. burst FOMs WaveMon can produce short latency FOMs – 1 min

  7. strain(t) (or range(t)) @ 50% detection efficiency for few selected waveforms (ad hoc & astro-motivated) BH-BH mergers (10, 50, 80 Mo) Gaussians (broadband) run-time simulation do injections with various strains run WM to find injections calculate strain at 50% detection efficiency report strain(t) at 1/minute rate latency – 1 min BH50 strain time, min Sensitivity FOM (2) glitches

  8. Lazarus (J.Baker et al, astro-ph/0202469v1) Effective one body (Damour, Iyer, Sathyaprakash) 0 200 400 600 800 Hz BH-BH merger waveforms Lazarus 30 Mo e=3% 10 ms

  9. S2 noise average over all sky BH-BH range very preliminary e=3%

  10. Noise outliers (FOM3) Compare Gaussian and non-parametric statistics H1 L1

  11. WaveMon Can help identify source of glitches in the GW channel by looking at correlation with auxiliary channels Produce veto triggers and rate trends Can produce meaningful FOM for astro-motivated waveforms  detectable burst strength(t) Plans incorporate real-time calibration for AS_Q channel implement simulation engine  strength(t) FOM get ready for S4 Summary & Plans

  12. Simple reconfiguring of the “double channel WM” Preserves all WaveMon functionality higher threshold (bpp ~ 1%) channel 1 channel 2,… wavelet transform, data conditioning rank statistics wavelet transform, data conditioning, rank statistics bp bp 1% channel 1 event generation channel 2 event generation Single channel WaveMon can be used to produce strain/range FOM for single IFO

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