Inspiral Waveform Consistency Tests

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Presentation transcript:

Inspiral Waveform Consistency Tests LIGO-G030669-00-Z Inspiral Waveform Consistency Tests Evan Ochsner and Peter Shawhan (U. of Chicago) (LIGO / Caltech) Gravitational Wave Data Analysis Workshop December 19, 2003 GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Peter Shawhan (LIGO/Caltech) The Standard 2 Test Divide template into p parts, each expected (on average) to contribute equally to the total SNR, and calculate a 2 : z and zl are complex numbers LSC inspiral analysis group has used p=8 in the past, currently is using p=14 GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Peter Shawhan (LIGO/Caltech) A Simulated Inspiral SNR 2 GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

The Loudest L1 Event in the S1 Analysis SNR 2 GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Why Do Garbage Events Survive the 2 Test ? The 2 test only uses a “slice” out of the time-freq plane SNR threshold is determined by noise averaged over job During a time interval with excess noise, the matched filter is likely to find some point in time with acceptable SNR & 2 Frequency Time GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Garbage Events Near a Big Glitch SNR “Inaccurate” inspiral coalescence times are understood to arise from ringing of the template filter 2 GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Additional Waveform Consistency Tests Look for excess noise just before the event time, using the matched filter output as a measure of noise in some way 16 Count number of time samples above a threshold, or number of threshold crossings, over some time interval Threshold=6.5 seems good for weak events SNR 6.5 GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Allow for Large Signals Use a threshold which depends on the peak SNR () 120 Large simulated signal SNR 21 6.5 GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Evaluate Tests Using S1 Data Modified filtering function in LAL to implement a few variations on these tests (Chosen based on examining several of the loudest events) Fixed vs. adjusted SNR threshold A few different time windows Re-ran the entire S1 inspiral analysis Analyzed full data set with Caltech LDAS Separate set of jobs with (software) injections, to calculate efficiency Stored triggers, with extra information, in database Studied effectiveness of the different test variations Test which seemed to provide best discrimination: number of crossings over adjusted SNR threshold GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Results for Simulated Signals 3 simulated events out of 2905 have 4 crossings Number of crossings SNR GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Peter Shawhan (LIGO/Caltech) Results for Data Number of crossings Cutting events with more than 4 crossings eliminates the loudest 13 events ! SNR GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)

Peter Shawhan (LIGO/Caltech) Summary and Plans A test of this sort would have cleaned up the S1 data Very clean – no inefficiency for signal ! (*** But tuned on these events) Reduced maximum SNR from 15.9 to 11.6 Rate limit would have improved from 170 to 140 per year per MWEG Needs to be properly incorporated into LAL Should probably develop a more robust way to deal with large signals Needs to be re-tuned using S2 playground data Hopefully, this will help the S2 analysis significantly Especially since we’ve had limited luck with auxiliary-channel vetoes GWDAW, 19 Dec 2003 Peter Shawhan (LIGO/Caltech)