slide1
Download
Skip this Video
Download Presentation
Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO and TAMA collaboration

Loading in 2 Seconds...

play fullscreen
1 / 36

Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO and TAMA collaboration - PowerPoint PPT Presentation


  • 64 Views
  • Uploaded on

Systematical veto analysis using all monitor signals. Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO and TAMA collaboration. 11th Gravitational Wave Data Analysis Workshop. Abstract. Purpose : fake rejection Method : systematical veto using all monitor signals

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO and TAMA collaboration' - mae


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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
Systematical veto analysis

using all monitor signals

Koji ISHIDOSHIRO, Masaki ANDO, Kimio TSUBONO

and

TAMA collaboration

11th Gravitational Wave Data Analysis Workshop

abstract
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.
contents
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
contents1
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
purpose
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
previous works
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.

our work
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

contents2
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
overview of our methods
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

event extraction
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

coincidence analysis
Coincidence analysis

GW candidate

Main signal

Burst events

Fake events

Coincidence

Monitor signal 1

Monitor signal 2

Time series

overview of our methods1
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

parameter optimization 1 2
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]

slide14
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.

signal selection
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.

example of signal selection 1 2
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.

example of signal selection 2 2
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.

contents3
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
tama300 data
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

safety of veto
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

contents4
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
selected signals
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

fake rate
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

contents5
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
summary
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
convert from power to h rss
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)

data conditioning
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

equivalent period
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

correlated signals
Correlated signals

Main signal

Laser

PD

Intensity

Normalized amplitude

Intensity signal

Time series

correlated signals1
Correlated signals

Main signal

Normalized amplitude

Magnetic fields

Time series

correlated signals2
Correlated signals

Main signal

Normalized amplitude

Vertical seismic motion

Time series

coincidence analysis1
Coincidence analysis

Power

output

Threshold

Time series data

Main signal

burst duration time

Threshold

Monitor signal

coincidence analysis2
Coincidence analysis

Power

output

Threshold

Time series data

Main signal

burst duration time

Threshold

Monitor signal

definition
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.

slide36
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.

ad