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Coherent Analysis by using multiple interferometric detectors. Norichika Sago (Osaka) with Hideyuki Tagoshi (Osaka) Hirotaka Takahashi (Osaka City) Nobuyuki Kanda (Osaka City) S. Dhurandhar (IUCAA). 6th Edoardo Amaldi Conference, June 20-24, 2005

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Coherent Analysis by using multiple interferometric detectors

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Coherent analysis by using multiple interferometric detectors l.jpg

Coherent Analysis by usingmultiple interferometric detectors

Norichika Sago (Osaka)

with

Hideyuki Tagoshi (Osaka)

Hirotaka Takahashi (Osaka City)

Nobuyuki Kanda (Osaka City)

S. Dhurandhar (IUCAA)

6th Edoardo Amaldi Conference, June 20-24, 2005

at Bankoku Shinryoukan, Okinawa, Japan


Introduction l.jpg

Introduction

Motivation

  • Many plan of GW detectors are on going in the world.

  • Japanese future plan : LCGT

(Large-scale Cryogenic Gravitational Telescope)

Better sensitivity

Fake-reduction

Two interferometers

An analysis method of multiple data is needed !

Analysis method

  • Coincident analysis

Make event list of each detector

Compare these lists

Fake reduction

  • Coherent analysis

Analyze a data set from a network

of detectors simultaneously

Improve sensitivity


Purpose of this work l.jpg

Purpose of this work

Estimate the improvement of detection efficiency

by using multiple detectors

  • Comparison with coincidence and coherence

  • Take account of correlation between detectors

  • Semi-analytic estimate


Assumption l.jpg

: output of each detector

assumption

injection signal : chirp signal

2-detectors :

  • same location, same orientation

  • same noise spectrum

  • stationary Gaussian noise

Output from a network of detectors

From the assumption (same location, same orientation),


Coherent analysis with two detectors no correlation between detectors l.jpg

: modifided Bessel function

Coherent analysis with two detectors(no correlation between detectors)

Finn, PRD 63, 102001 (2001)

Pai, Dhurandhar and Bose, PRD 64, 042004 (2001)

  • SNR of 2-detector network

From the assumption,

  • Probability distribution of SNR

For data with signal :

For no signal data :


Slide6 l.jpg

  • false alarm rate

(alert a detection for data without signal)

number of independent

templates

false alarm probability for

a single template

We can calculate the threshold for a given false alarm rate:

  • false dismissal probability

(alert no detection for data with signal)

The detection efficiency is given by:


Coincident analysis case l.jpg

Coincident analysis case

  • false alarm rate

no window case

Taking account of parameter window

increasing the number of template

(Here, we assume Nwin=10.)

  • detection efficiency

: False dismissal rate for single detector


Number of template l.jpg

Number of template

  • considered parameter

mass of binary :

coalescence time :

  • estimate of the number of template

number of data point

number of template in mass space

Owen (’95)

for LCGT case

MM : minimal match (=0.97)

f0 : frequency at minimal noise spectrum

mmin : minimal mass for search

maximum mass for search :


Lower limit of detection efficiency l.jpg

Lower limit of detection efficiency

2 detector (LCGT)

1-yr observation

stationary Gaussian noise

no correlation between detectors

All templates are independent.

We assume Nwin = 10.

detection probability

For

For

false alarm rate


More accurate false alarm rate l.jpg

More accurate false alarm rate

Actually, templates correlate each other.

: parameter set

Overestimate of false alarm rate

If events A and B are dependent,

We can estimate the more accurate false alarm rate

by considering the independent portion of all templates.


Number of independent templates l.jpg

Number of independent templates

  • correlation in time

We regard that two templates are independent if the difference

of the coalescence time between them is larger than 8 msec.

( Match of them is sufficiently small. )

  • correlation in mass space

Here we consider the cases that 1%, 10% and 50% of all templates

are independent, respectively.

(A estimate by simulations is needed for a more realistic evaluation.)


More accurate detection efficiency l.jpg

More accurate detection efficiency

2detectors, coherent

If we take account of the cor-

relation between templates,

the number of independent

templates decreases.

The detection efficiency increases.

detection probability

false alarm rate


Two detectors with correlation l.jpg

Two detectors with correlation

  • correlation in same frequency (2detectors)

  • diagonalization of noise matrix

We define a new set of data with a linear combination of bare data.

Here,

The psuedo-detector 1 contains no signal.

We can regard the case that a signal with amplitude,

is injected into a detector with


Detection efficiency correlated detectors l.jpg

Detection efficiency (correlated detectors)

correlation factor between

two detectors, e

Here we assume the phase of

signal is known in advance.

detection efficiency

solid line : coherence

dashed line : coincidence

(Nwin = 10)

The blue, green and red lines

show the cases of

black solid : single detector

false alarm rate


Summary and future works l.jpg

Summary and Future works

  • Detection efficiency of coherent analysis is better than the

  • one of coincident analysis in stationary Gaussian noise case.

  • Correlation between detectors makes the efficiency get worse.

  • However, if the detectors’ correlation is less than 10%, they

  • can observe better efficiently than by a single one.

Future works

  • Simulation to estimate the more accurate detection efficiency

  • Analysis with more realistic noise

  • (non-Gaussian, non-stationary, …)


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