Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO and

TAMA collaboration

11th Gravitational Wave Data Analysis Workshop

Systematical veto analysisSystematical veto analysis using all monitor signals using all monitor signals

Abstract

Purpose : fake rejection

Method : systematical veto using all monitor signals

Application: TAMA300 DT9

Results : fake rate was improved 2 orders.

• coincidence analysis (event-by-event veto)• systematical veto setting

ContentsIntroduction

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

ContentsIntroduction

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

PurposePurpose

Fake rejection using monitor signals

Main signal

Intensity signal

Time series

Norm

alized

am

plitu

de

Monitor signals• L+• l-• l+• Laser intensity• Dark-port power• Bright-port power• Seismic motion• Magnetic field and so on

Monitor signals are recorded with a main output signal of a detector to watch instabilities of the detector.

Previous works

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.

Previous monitor signal analysis

Many monitor signals are recorded.

Monitor signals

We should use all monitor signals with optimal parameters. We must optimize many veto parameters. It is difficult to optimize them for instant.

Our workMethod: systematical veto using all monitor signals

parameter optimization signal selection

Monitor signal1

Monitor signal2

Monitor signal3

Monitor signal4

・・・

Signal Selection

Parameter Optimization

•High efficiency•Low accidentalcoincidence rate

X

X

Systematical veto setting

Main signal

Coincidence Analysis

(event-by-event veto)

Coincidence analysis (event-by-event veto)

Systematical veto setting

Application: TAMA300 DT9

ContentsIntroduction

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

Overview of our methods

Main signalMain signal Monitor signalsMonitor signals

Data conditioningData conditioning-whitening, removal of lines--whitening, removal of lines-

Event extraction

Coincidence analysisCoincidence analysis(often called event-by-event veto)(often called event-by-event veto)

Fake eventsFake eventsGW candidatesGW candidates

Without coincidenceWithout coincidence

vetoveto

With coincidenceWith coincidence

- - Excess-power filter (Δt,Δf, Pth) -W.G. Anderson (2001,1999)

Signal Selection

Parameter optimization

Systematical veto setting

Event extractionExcess-power filter calculates signal power in a given time-frequency window.

When power is larger than a given threshold, we detect burst event.

Main signal time window Δt : 12.8 msec frequency window Δf :800 – 2000 Hz Monitor signals time window Δt frequency window Δf power threshold Pth

optimization

W.G. Anderson (2001,1999)

burst event

Coincidence analysis

Main signal

Monitor signal 1

Burst events

Coincidence

Fake events

Monitor signal 2

GW candidate

Time series

Overview of our methods

Main signalMain signal Monitor signalsMonitor signals

Data conditioningData conditioning-whitening, removal of lines--whitening, removal of lines-

Event extraction

Coincidence analysisCoincidence analysis(often called event-by-event veto)(often called event-by-event veto)

Fake eventsFake eventsGW candidatesGW candidates

Without coincidenceWithout coincidence

vetoveto

With coincidenceWith coincidence

- - Excess-power filter (Δt,Δf, Pth) -W.G. Anderson (2001,1999)

Signal Selection

Parameter optimization

Systematical veto setting

Parameter optimization 1/2

Accidental coincidence rate is estimated by 1-min.time-shifted data.

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.

Veto efficiency =2/3

Accidental coincidencesrate =1/3

Time series [min]

Am

plitu

de

Main signal

Monitor signalTime-shifted monitor signal

1. Pth is fixed in a given 　 Δt, 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.

Parameter optimization 2/2

Accidental coincidence rate

Veto efficiency

0.1%

Power threshold 101

1%

10%

100%rate

0.1% is fixed so that total accidental coincidence rate is enough small.

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.

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%

We would like to use the monitor signals having strong correlation with the main signal more effectively.

These monitor signals are re-optimized so that the power threshold become lower.

Example of Signal selection 1/2

Monitor signal do not have significant correlation.rate

100%

10%

1%

0.1%

Power

simulated data

We do not use this signal for veto.

Veto efficiency

Accidental coincidence rate

Example of Signal selection 2/2

Monitor signal have strong correlation.rate

100%

10%

1%

0.1%

Power

simulated data0.01%

We use this signal for veto with lower threshold.

Veto efficiency

Accidental coincidence rate

ContentsIntroduction

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

TAMA300 dataData: 200 hours in 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

Safety of vetoHuge GWs 　 may 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.

We compared veto efficiencyand accidental coincidence rate.

Threshold

rate

Accidentalcoincidence

1 sigmaSignificant differences did not exit for all monitor signals.

Even huge GWs did not makeburst events on monitor signals.

Veto efficiency

ContentsIntroduction

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

Selected signals

Laser

Bright-port PowPD

L+

Trans. Pow

Dark-port Pow

Intensity

• SEIS Z • Magnetic field

l-, l+

These 10 monitor signals were selected.

Intensity and l- signals were re-optimized to have lower threshold.

Selected monitor signals• Laser intensity• l-• L+• l+• Dark-port power• Bright-port power• Seismic motion• Magnetic field

Fake rate

Without veto

With veto

hrss threshold

Fake r

ate

[H

z]

Fake rate was improved 2 orders @ hrss = 10-18 .Maximum amplitude of fakes was improved by 1/4 .

Power threshold

hrss threshold

Software injection test

1/100

1/4

Accidental coincidence rate3.2%

Dead time0.2%

ContentsIntroduction

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

SummarySystematical 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=10-18 with 3.2% accidental coincidence rate (or 0.2% dead time).

Future works• We would like to apply this method to online study for TAMA300 and CLIO

• Understandings of fakes origin were obtained. (such as unexpected correlation)

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

Log(hrss)

Power

Injection events

Fitting lineFitting line

Data conditioningRaw data: non-stationary, frequency dependence, line noises

Data conditioning filter

Normalization by averaged power before 10 min

Removal lines

Selection frequency bands to be analyzed

Fourier domain

Frequency

power

Time series

Before data condition

Afterdata condition

Equivalent period

Time series

Amplitude

375Hz signal

20kHz signal

Different sampling signal: out of synchronization

High speed DAQ 20kHz, Middle speed DAQ 375Hz

Common signal: dark-port power

Minimum T

Correlated signals

Main signal

Intensity signal

Time series

Norm

alized

am

plitu

de

PD

Laser

Intensity

Correlated signals

Main signal

Magnetic fields

Time series

Norm

alized

am

plitu

de

Correlated signals

Time series

Main signal

Norm

alized

am

plitu

de

Vertical seismic motion

Coincidence analysis

Threshold

Time series data

Poweroutput

burst duration time Main signal

Threshold

Monitor signal

Coincidence analysis

Threshold

Time series data

Poweroutput

burst duration time Main signal

Threshold

Monitor signal

DefinitionVeto 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.

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.