1. Background estimation in searches for binary inspiral
Patrick Brady
Inspiral Working Group
LIGO Scientific Collaboration

2. Inspiral search pipeline
Data Quality cuts applied up front
Analyze only science mode data
Multi-interferometer follow up on coincident triggers.
(Talk by Bose)
Single interferometer vetoes applied to triggers from each interferometer
(Talk by Christensen and Shawhan)
Background Estimation
Time slide Hanford data relative to Livingston data
11/10/2003
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3. Coincidence criteria
Triggers from Livingston are used to search the corresponding Hanford data
Check for coincidence between the Livingston and Hanford triggers.
The coincidence algorithm was:
1. Triggers must be coincident in time to within 11 ms.
2. Each mass parameter in the template must be the same to within 0.03 solar masses.
3. Compare distances measured at Livingston (DL) and Hanford (DH)
11/10/2003
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4. Tuning the amplitude cut
Errors in distance estimates expected to decrease with increasing SNR
e and k tunable constants
DH/DL
Missed injections in coincidence
|DL - DH|/DL < e/rH + k
Relax amplitude cut to allow detection of these Milky Way events
11/10/2003
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5. Time-lag results: preliminary results using small bank
LLO triggers in coincidence with LHO
Take a closer look at these candidates
Livingston SNR
LHO triggers in coincidence with LLO
11/10/2003
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Hanford SNR

6. Time-lag analysis: SNR in each detector
Coherent two detector SNR=10
Hanford SNR
Large SNR in Livingston coincident with low SNR at Hanford
Livingston SNR
11/10/2003
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7. Time-lag analysis: triggers versus injections
Simulated injections of binary inspirals in Andromeda
Raise SNR threshold in Hanford to 6
Hanford SNR
Livingston SNR
11/10/2003
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8. Time-lag analysis: full bank & adjusted thresholds
No. of distinct candidates after 1 second clustering
Two out of 18 time-lags have coincidence in templates of same mass
11/10/2003
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9. Time-lag analysis: full bank & adjusted thresholds
11/10/2003
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10. Concluding remarks Where to next: Search for binary inspiral
Complete ~100 time-shifts on playground data
Check for problems with background estimation using time-shifts
Run time-shift analysis on all data to be used in search
determine network SNR threshold
compare to extrapolation from playground
Search for binary inspiral
Use background estimate from time-shifts on full data set
Search for candidate events
Determine rate limit based on efficiency, background, and number of detections
11/10/2003
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