TAMA binary inspiral event search

TAMA binary inspiral event search

Hideyuki Tagoshi (Osaka Univ., Japan)

3rd TAMA symposium, ICRR, 2/6/2003

Coalescing compact binaries

Neutron starsBlack holes

Inspiral phase of coalescing compact binaries are main target because

Expected event rate of NS-NS merger: a few within 200Mpc /year

Well known waveform, etc.

Possibility of MACHO black holes

TAMA Binary inspiral search

1. Neutron star binary search

2. TAMA-LISM coincident event search for mass range (onestep search)

3. Lower mass

4. Higher mass

1 2M M

1 2M M

0.2M

10M

Matched filter• Detector outputs:

：　 known gravitational waveform (template)

： noise.

• Outputs of matched filter:

• noise spectrum density

• signal to noise ratio

• Matched filtering is the process to find optimal

parameters which realize

s t Ah t n t( ) ( ) ( ) h t( )n t( )

( , , ,...)~( )

~( )

( )

*

m m ts f h f

S fdfc

n1 2 2 z

max ( , , ,...), , ,...m m t

cc

m m t1 2

1 2FH IK

SNR = / 2

Post-Newtonian approximation

( )nS f

Matched filtering analysis

tRead data

FFT of dataApply transfer function

Conversion to stain equivalent data

Evaluate noise spectrum near the data( )nS f

　　　　　　　　( , , )ct M

max ( , , ) c

ct

t M

( 25 )ct ms

52 sec

Event list(only 7 events)

　　　　　　　　2( , , )ct M

( 7)if

,max ( , , )

c

Mt M

2 ( / )S N

TAMA events and Galactic event

Test signals

selection will produce loss of strong S/N events

2

2/ 16 T

AM

A e

vent

s

2

Search Result TAMA DT6

2/

Log

10[N

umbe

r of

eve

nts]

2/ 16

Upper limit to the Galactic event rate

N

T •Ｎ：　 Upper limit to the average number of events

over certain threshold

•Ｔ：　 Length of data [hours]

• : 　 Detection efficiency

Galactic event simulationWe perform Galactic event simulation to estimate detection efficiency

Assume binary neutron stars distribution in our Galaxy

2 20/ 2 / zR R Z hdN e e RdRdZ

0 4.8 kpc

1 kpcz

R

h

•Give a time during DT6

•Determine mass, position, inclination angle, phase by random numbers

•Give a test signal into real data

•Search

•Make event lists and estimate detection efficiency

Mass : distribute uniformly between 1 2M

Galactic event detection efficiency

2/ 16 0.23

Upper limit to the event rate: Poisson statistics

•Threshold ( )

•Expected number of fake events over threshold ： Nbg=0.1

•Observed number of events over threshold: Nobs=0

Assuming Poisson distribution for the number of real/fake events

over the threshold,

we obtain upper limit to the expected number of real events from( )

0

0

( )

!1

( )

!

obsbg

obsbg

nn Nx N bg

nnn N

N bg

n

x Ne

nCL

Ne

n

N=2.3　 (C.L.=90%)

2/ 16

Upper limit to the Galactic event rate

threshold=16 ( ～ S/N=11)

(fake event rate=0.8/year)

Efficiency

•We also obtain upper limit to the average number of events over threshold by standard Poisson statistics analysis

N=2.3 　 (C.L.=90%)

•Observation time T ＝ 1039 hours

0.23

0.0095 [1/ hour] ( . . 90%)N

C LT

c.f. 　　 Caltech 40m : 0.5/hour 　　 (C.L.=90%)　　　　　　　 Allen et al. Phys. Rev. Lett. 83, 1498 (1999).

TAMA DT7: 2002.8.31 ～ 2002.9.2

Best Sensitivity:

DT7 analysis

213.3 10 / Hz

DT7 event lists

These results will be used for TAMA-LIGO coincidence analysis.

23.7 hours data

2

Divide frequency region into bins.Test whether the contribution to from each bins agree with that expected from chirp signal

fminf1 f2 f3 f4 f5 fmax

1 2 3 4 5

FHG

IKJ z( , )

~( )~

( )

( )

*

s hs f h f

S fdf

n

2

22

2

2 2

1

i

i i

i i i i i

( )

( ) ,

chi square

[1.09minutes]

max

min

1/ 27 / 3

4( )

f

fn

fdf

S f

min max100Hz, f 2500Hzf TAMA DT6 all 8/1 ～ 9/20/2001

Variation of Noise power (1 minute average)

max

min

1/ 27 / 3

4( )

f

fn

fdf

S f

min max100Hz, f 2500Hzf LISM DT6 9/3 ～ 9/17/2001

Variation of Noise power (1 minute average)

[1.09minutes]

•Binary inspiral search : one step search (Tagoshi, Tatsumi,Takahashi)

TAMA-LISM coincidence

(Takahashi,Tagoshi,Tatsumi)

two step search (Tagoshi, Tanaka)

•Binary inspiral search using Wavelet: (Kanda)

•Continuous wave from known pulsar: (Soida, Ando)

•Burst wave search: (Ando)

•Noise veto analysis: (Kanda)

•Calibration: (Tatsumi, Telada,…)

•Interferometer online diagnostic: (Ando,…)

•BH ringdown search, Stochastic background search, etc. will be done.

•Two new post-docs (Tsunesasa(NAOJ),Nakano(Osaka))

TAMA data analysis activity