TAMA binary inspiral event s earch Hideyuki Tagoshi (Osaka Univ., Japan) 3rd TAMA symposium, ICRR, 2/6/2003
Jan 13, 2016
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