1 / 15

Performance of the WaveBurst algorithm on LIGO S2 playground data

Performance of the WaveBurst algorithm on LIGO S2 playground data S.Klimenko (UF), I.Yakushin (LLO), G.Mitselmakher (UF), M.Rakhmanov(UF) for LIGO collaboration Introduction Trigger production Triple coincidence Simulation sine-Gaussian BH-BH mergers Summary.

larya
Télécharger la présentation

Performance of the WaveBurst algorithm on LIGO S2 playground data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Performance of the WaveBurst algorithm on LIGO S2 playground data S.Klimenko (UF),I.Yakushin (LLO), G.Mitselmakher (UF), M.Rakhmanov(UF) for LIGO collaboration Introduction Trigger production Triple coincidence Simulation sine-Gaussian BH-BH mergers Summary

  2. WaveBurst (see S.Klimenko’s talk for details, GWDAW, December 20, 2003) excess power detection method in wavelet domain flexible tiling of the TF-plane by using wavelet packets variety of basis waveforms for burst approximation local in time & frequency, low spectral leakage use rank statistics  non-parametric use local T-F coincidence rules for multiple IFOs coincidence applied before triggers are produced works better for 2 and more interferometers (but can do analysis with one interferometer as well) Symlet 58 packet (4,7) Symlet 58 Introduction

  3. LIGO data H2:LSC-AS_Q Wavelet time-scale(frequency) spectrogram WaveBurst allows different tiling schemes including linear and dyadic wavelet scale resolution. currently linear scale resolution is used (Df=const)

  4. channel 1 channel 2,… coincidence TF1 wavelet transform, data conditioning rank statistics wavelet transform, data conditioning, rank statistics bp “coincidence” bp bp TF2 IFO1 event generation IFO2 event generation WaveBurst pipeline band 64-4096 Hz selection cuts: coincidence likelihood L>1.5, cluster likelihood L>4  selection of black pixels (10% loudest)

  5. expect reduce background down to <10 mHz using final selection cuts: r-statistics, event confidence, veto, … double coincidence samples (S2 playground) ifo pair L1-H1 H1-H2 H2-L1 triggers 29346 22469 36956 lock,sec 94652 98517 93699 rate, Hz 0.31 0.23 0.39 raw triple coincidence rates Raw Coincidence Rates off-time samples are produced during the production stage independent on GW samples triple coincidence: time window: 20 ms frequency gap: 0 Hz  1.10± 0.04 mHz

  6. off-time triple coincidence sample raw triple coincidence rates “BH-BH merger” band expect BH-BH mergers (masses >10 Mo) in frequency band < 1 kHz (BH-BH band) S2 playground background of 0.15 ± 0.02 mHz expect < 1 mHz after final cuts

  7. hardware injections software injection into all three interferometers: waveform name GPS time of injection {q, j,Y}          -  source location and polarization angle T {L1,H1,H2}  -  LLO-LHO delays F+{L1,H1,H2}  -  + polarization beam pattern vector Fx {L1,H1,H2}   -  x polarization beam pattern vector use exactly the same pipeline for processing of GW and simulation triggers. sine-Gaussian injections 16 waveforms: 8-Q9 and 8-Q3 F+ {1,1,1} , Fx {0,0,0} BH-BH mergers (10-100 Mo) 10 pairs of Lazarus waveforms {h+,hx} all sky uniform distribution with calculation {F+,Fx} for LLO,LHO t –duration f0-central frequency Simulation

  8. hardware injections SG injections [ 100Hz, 153Hz , 235Hz, 361Hz, 554Hz, 850Hz, 1304Hz2000Hz ] good agreement between injected and reconstructed hrss good time and frequency resolution H1H2 pair

  9. hrss(50%) @235 Hz robust with respect to waveform Q fo, Hz 100 153 235 361 554 850 1034 2000 h50%, Q9 40. 20. 4.8 7.5 7.2 - 16. - h50% , Q3 36. 14. 6.0 6.6 8.6 10. 17. 30. x10-21 x10-21 detection efficiency vs hrss

  10. time window >= 20 ms  negligible loss of simulated events (< 1%) 1% loss 12% loss timing resolution S2 playground simulation sample sT=4ms

  11. Use orthogonal wavelet (energy conserved) and calibration. frequency mean amplitude reconstructed log10(hrss) Signal reconstruction

  12. BH-BH mergers(Flanagan, Hughes: gr-qc/9701039v2 1997) duration : start frequency : bandwidth: Lazarus waveforms (J.Baker et al, astro-ph/0202469v1) (J.Baker et al, astro-ph/0305287v1) BH-BH merger injections all sky simulation using two polarizations and L & H beam pattern functions

  13. mass, Mo 10 20 30 40 50 60 70 80 100 hrss(50%) x 10-20 4.5 2.4 2.0 1.8 1.5 1.7 2.2 3.4 7.1 Lazarus waveforms: efficiency all sky search: hrss(50%)

  14. expected BH-BH frequency band – 100-1000 Hz Lazarus waveforms: frequency vs mass

  15. analysis pipeline is fully operational (production, post-production, simulation). robust detection of SG waveforms with different Q pipeline sensitivity (no data conditioning yet) (5-20) . 10-21 - optimal detection (SG waveforms). ~2 . 10-20 - all sky BBH merger search (Lazarus waveforms)  plan efficiency study using EOB & ABFH merger waveforms background: raw triple coincidence rates full band (4kHz): ~1 mHz “BH-BH band” (<1kHz): ~0.15 mHz  after all selection cuts expect <1 mHz background rate for full S2 data set Summary

More Related