Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO and TAMA collaboration

1. Systematical veto analysis using all monitor signals Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO and TAMA collaboration 11th Gravitational Wave Data Analysis Workshop

2. Abstract Purpose : fake rejection Method : systematical veto using all monitor signals Application: TAMA300 DT9 Results : • coincidence analysis (event-by-event veto) • systematical veto setting • fake rate was improved 2 orders.

3. Contents • Introduction purpose, previous works, our work • Method coincidence analysis (event-by-event veto), systematical veto setting • Data TAMA data, safety of veto • Results signals selection, fake rejection • Summary

4. Contents • Introduction purpose, previous works, our work • Method coincidence analysis (event-by-event veto), systematical veto setting • Data TAMA data, safety of veto • Results signals selection, fake rejection • Summary

5. Main signal Normalized amplitude Intensity signal Time series Purpose Purpose Fake rejection using monitor signals Monitor signals are recorded with a main output signal of a detector to watch instabilities of the detector. • Monitor signals • L+ • l- • l+ • Laser intensity • Dark-port power • Bright-port power • Seismic motion • Magnetic field • and so on

6. Previous works Previous monitor signal analysis Monitor signals have been investigated for fake rejection and detector characterization. A. D. Credico (2005), P. Ajith (2006), M. Ando (2005) • They have used only monitor signals having well known correlation with the main signal. • They have optimized veto parameters by hands. Monitor signals Manymonitor signals are recorded. We should use all monitor signals with optimal parameters. We must optimize many veto parameters. It is difficult to optimize them for instant.

7. Our work Method: systematical veto using all monitor signals Systematical veto setting • parameter optimization • signal selection Coincidence analysis (event-by-event veto) Application: TAMA300 DT9 Systematical veto setting Main signal Signal Selection • Parameter • Optimization • High efficiency • Low accidental • coincidence rate Coincidence Analysis (event-by-event veto) Monitor signal1 X Monitor signal2 X Monitor signal3 Monitor signal4 ・ ・ ・

8. Contents • Introduction purpose, previous works, our work • Method coincidence analysis (event-by-event veto), systematical veto setting • Data TAMA data, safety of veto • Results signals selection, fake rejection • Summary

9. Overview of our methods Main signal Monitor signals Signal Selection Data conditioning -whitening, removal of lines- Systematical veto setting Event extraction Parameter optimization - Excess-power filter(Δt,Δf, Pth) - W.G. Anderson (2001,1999) Coincidence analysis (often called event-by-event veto) Without coincidence With coincidence GW candidates Fake events veto

10. Event extraction Excess-power filter calculates signal power in a given time-frequency window. When power is larger than a given threshold, we detect burst event. W.G. Anderson (2001,1999) Main signal time window Δt : 12.8 msec frequency window Δf :800 – 2000 Hz burst event Monitor signals time window Δt frequency window Δf power threshold Pth optimization

11. Coincidence analysis GW candidate Main signal Burst events Fake events Coincidence Monitor signal 1 Monitor signal 2 Time series

12. Overview of our methods Main signal Monitor signals Signal Selection Data conditioning -whitening, removal of lines- Systematical veto setting Event extraction Parameter optimization - Excess-power filter(Δt,Δf, Pth) - W.G. Anderson (2001,1999) Coincidence analysis (often called event-by-event veto) Without coincidence With coincidence GW candidates Fake events veto

13. Parameter optimization 1/2 • Monitor signal • Δt, Δf • Pth • Optimization in systematical setting • high veto efficiency • low accidental coincidence rate 20 hours data is used only for systematical veto setting. 20 hours is the least time for us to get statistically-significant. Accidental coincidence rate is estimated by 1-min.time-shifted data. Main signal Amplitude Veto efficiency =2/3 Time-shifted monitor signal Monitor signal Accidental coincidences rate =1/3 Time series [min]

14. rate 100% Veto efficiency 10% Accidental coincidence rate 1% 0.1% 1 Power threshold 10 Parameter optimization 2/2 • Pth is fixed in a givenΔt, Δf • so that accidental coincidence rate is 0.1%. • 2. Veto efficiency is calculated using the fixed threshold. • 3. These processes are repeated using different Δt, Δf 100 times. • 4. The Δt, Δf having • the highest efficiency are • selected as optimal parameters. 0.1% is fixed so that total accidental coincidence rate is enough small.

15. Selection by the veto efficiency <0.5 %  Do not use for veto 0.5 - 2 %  Use for veto > 2%  Use for veto with re-optimization lower threshold: accidental ~ 0.5% Signal selection Using the monitor signals having no correlation make accidental coincidence rate increase without improvement of veto efficiency. The monitor signals must be selected to be used for veto or not. We would like to usethe monitor signals having strong correlation with the main signal more effectively. These monitor signals are re-optimized so that the power threshold become lower.

16. Example of Signal selection 1/2 Monitor signal do not have significant correlation. rate 100% Veto efficiency 10% Accidental coincidence rate 1% 0.1% simulated data Power We do not use this signal for veto.

17. Example of Signal selection 2/2 Monitor signal have strong correlation. rate 100% Veto efficiency 10% Accidental coincidence rate 1% 0.1% simulated data 0.01% Power We use this signal for veto with lower threshold.

18. Contents • Introduction purpose, previous works, our work • Method coincidence analysis (event-by-event veto), systematical veto setting • Data TAMA data, safety of veto • Results signals selection, fake rejection • Summary

19. TAMA300 data Data: 200 hoursin TAMA DT9 (Dec. 2003 – Jan. 2004) (20 hours data is used for only parameter optimization) Monitor signals: 64 channels (HDAQ 3ch, MDAQ 61ch) HDAQ: 20kHz, 16bit MDAQ: 316.5Hz, 16bit

20. Safety of veto Huge GWsmay make burst events on monitor signals. We confirmed the safety of veto by hardware injection test during DT8 and after DT9. Sine Gaussian waves were injected into L- feedback signal. rate Veto efficiency We compared veto efficiency and accidental coincidence rate. 1 sigma Significant differences did not exit for all monitor signals. Accidental coincidence Even huge GWs did not make burst events on monitor signals. Threshold

21. Contents • Introduction purpose, previous works, our work • Method coincidence analysis (event-by-event veto), systematical veto setting • Data TAMA data, safety of veto • Results signals selection, fake rejection • Summary

22. Selected signals These 10 monitor signals were selected. Intensity and l- signals were re-optimized to have lower threshold. L+ • SEIS Z • Magnetic field • Selected monitor signals • Laser intensity • l- • L+ • l+ • Dark-port power • Bright-port power • Seismic motion • Magnetic field l-, l+ Intensity Laser Trans. Pow Bright-port Pow PD Dark-port Pow

23. Fake rate Fake rate was improved 2 orders @ hrss = 10-18 . Maximum amplitude of fakes was improved by 1/4 . Accidental coincidence rate 3.2% Dead time 0.2% Without veto Fake rate [Hz] Power threshold 1/100 With veto Software injection test 1/4 hrss threshold hrss threshold

24. Contents • Introduction purpose, previous works, our work • Method coincidence analysis (event-by-event veto), systematical veto setting • Data TAMA data, safety of veto • Results signals selection, fake rejection • Summary

25. Summary Systematical veto method using all monitor signal coincidence analysis (event-by-event veto) systematical veto setting Analysis with TAMA DT9 data • 200 hours data (10% are used for only parameter optimize) • 10 monitor signals were selected. • Fake rate was improved 2 orders @hrss=10-18with • 3.2% accidental coincidence rate (or 0.2% dead time). • Understandings of fakes origin were obtained. • (such as unexpected correlation) Future works • We would like to apply this method to online study • for TAMA300 and CLIO

27. Convert from power to hrss Signal power: dimensionless signal-to-noise ratio → physical value: GWs RSS (root-sum-square) amplitude Software injection test: sine-Gaussian signals f=850Hz, 1304Hz, Q=8.9 Power Fitting line Fitting line Injection events Log(hrss)

28. Data conditioning Raw data: non-stationary, frequency dependence, line noises Data conditioning filter Fourier domain Normalization by averaged power before 10 min Removal lines Selection frequency bands to be analyzed power Before data condition After data condition Time series Frequency

29. Equivalent period Different sampling signal: out of synchronization High speed DAQ 20kHz, Middle speed DAQ 375Hz Common signal: dark-port power Amplitude 375Hz signal Minimum T 20kHz signal Time series

30. Correlated signals Main signal Laser PD Intensity Normalized amplitude Intensity signal Time series

31. Correlated signals Main signal Normalized amplitude Magnetic fields Time series

32. Correlated signals Main signal Normalized amplitude Vertical seismic motion Time series

33. Coincidence analysis Power output Threshold Time series data Main signal burst duration time Threshold Monitor signal

34. Coincidence analysis Power output Threshold Time series data Main signal burst duration time Threshold Monitor signal

35. Definition Veto efficiency: the rate of burst events rejected Accidental coincidence rate: the probability of burst events rejected accidentally Accidental coincidence rate was estimated by four different time-shifted data. Significant differences did not exit. We select 1 min. time shift for easy.

36. Time Total: 200 hours ⇒ 180 hours are used to set an upper limit. 20 hours are used only to set veto parameters. ⇒ 200 hours are used to search evens.