Issues in GW bursts Detection Soumya D. Mohanty AEI

1. Issues in GW bursts DetectionSoumya D. MohantyAEI Outline of the talk • Transient Tests (Transient=Burst) • Establishing Confidence in Detection • Upper Limits • Detector Characterization GEO data analysis meeting, Golm

2. Transient Tests • Several Tests have been developed. • No uniformly most powerful transient detector known. • Need detectors that can exploit NR waveform information. • Thorough comparison required. • One such project is already underway. (SDM, Patrice Hello, Eric Chassande-Motin.) • Criteria for comparison • Receiver Operating Characteristics (ROC). • Robustness against noise models. • Robustness against signal models. • Computational requirements. GEO data analysis meeting, Golm

3. Establishing Confidence in Detection • Confidence: • Probability of detected event being not due to noise. • 1 - (False alarm probability). • What should be our level of confidence? • False alarms: Terrestrial burst interference, instrumental noise. • Primary means of establishing confidence • Coincidence between GW interferometers. • Coincidence between GW and Astronomical observations. • Anti-coincidence with auxiliary channels. GEO data analysis meeting, Golm

4. Coincidence: GW detectors (I) • False alarm rate (noise) in pairwise coincidence • = r1 r2w. • r1, r2 = false alarm rates in each detector. w = window size. • Detection probability = Q1 Q2 p. • Important:p comes from time of arrival estimation error. (It is also direction dependent.) • Estimation errors are threshold independent. For givenSNR, Detection probability <= p . • p can become small if w isnot sufficiently greater than . GEO data analysis meeting, Golm

5. Coincidence: GW Detectors (II) • For transients, time of arrival estimates will not be as good as for known waveform signals. • Will not be a surprise if the error is comparable to light travel time. • Need a good estimate of the time of arrival estimate error variance. • Monte Carlo simulations required for complicated tests. • Detector characteristics used in simulations change with time. GEO data analysis meeting, Golm

6. Coincidence: GW Detectors (III) • How does SNR in coincidence scale with number of detectors ? • Coincidence cannot beat (Number of detectors)½ scaling of SNR in Likelihood Ratio tests. • Coincidences possible in other parameters • Each extra coincidence parameter means reduction in detection probability. • Note: As before, there will be a threshold independent limit on detection probability. • Estimation error covariance matrix required for correct window volume. GEO data analysis meeting, Golm

7. Coincidence: GW Detectors (IV) • Bar detectors Strategy: Detect an excess in coincidences. • Considerable experience available. • Do some of the same concerns apply? • Other issues • Differing detector characteristics (e.g., noise PSD). • Cross-correlation based methods: Have to rule out terrestrial sources of cross-correlation, SNR goes as N¼. GEO data analysis meeting, Golm

8. Coincidence: GW with Astro. (I) • Coincidence window in time for GW and Astro. event will not be known in general. • Method under development by Soma Mukherjee and SDM that makes window size a parameter that can be searched over for the best fit to the observed rates. • Can be generalized to include positional information. • Positional coincidence: Too big an error box is a problem for counterpart searches. • Could do something like ROTSE or LOTIS: Fast, wide-field optical searches by robotic telescopes. • What is the confidence level required by astronomers? • Even if a candidate is found, may need to estimate GW burst waveform to form a plausible link. GEO data analysis meeting, Golm

9. Coincidence: GW and Astro. (II) • Use astronomical triggers to look for GW bursts. • Reduces data duration for search and, hence, false alarm rate (increases confidence). • Analysis for Binary Inspirals by Piran (1993). • Need to put a selection on triggers. • Example: Most GRBs occur at large distances. Putting a distance cutoff can enhance confidence. • Statistical Detection possible. • Finn, Mohanty, Romano, 1999. • Use triggers to “chop” between background and source and check for statistical difference. • Useful quantities can be calculated such as average GW burst strength. GEO data analysis meeting, Golm

10. Anti-coincidence with Aux. Channels (I) • Simple analysis. • For N aux. channels per site with false alarm rate of r in each channel, probability of at least one false anti-coincidence = 2 N r w. (w is the time window). • w = 2 sec, N = 10, r = 5/hour gives 1 out of 18 GW bursts falsely dismissed. • False alarm rate for aux. channels may have large error • Example: actual r could be 10/hour: 1 per 10 GW bursts lost. • Robust test: false alarm rate independent of noise models (SDM, 2000). • This could also be an issue for GW-GW coincidence since false alarm rate r1r2w goes as square of individual false alarm rate. GEO data analysis meeting, Golm

11. Robust Transient Test • SDM, PRD, 2000. • False alarm rate is independent of noise model. • Made possible by • Adopting the weakest possible criterion for a transient: Brief episode of non-stationarity. No mention of Gaussianity or non-Gaussianity here. • Check for a change in the (only) measure of (wide sense) stationarity which is the PSD. • Surprisingly good performance. • Can be improved by reducing robustness (= less general criterion for a transient). GEO data analysis meeting, Golm

12. Anti-coincidence with Aux. Channels (II) • Clearly, a simple anti-coincidence is not enough • Need to establish if an aux. channel event could have caused the GW channel transient. • May require an end-to-end simulation software as being developed in the LIGO project. • Waveform estimation tools required. • Estimation also occurs in designing a proper GW-GW coincidence scheme (eg.,time of arrival estimation error). • Estimation seems to be intimately intertwined with detection. GEO data analysis meeting, Golm

13. Upper Limits • Upper limit on what? • Rate at a certain SNR, in some Bandwidth, in a certain class of bursts. • Max. SNR in a certain class of bursts. • Other quantities of Astrophysical interests. • Each quantity above requires its own analysis scheme. • Combining upper limits on rates with coincidence analyses. • Much to learn from the Bar detector community. • Important to characterize background (rate, distribution of amplitude etc.) • One possible way: Treat all non-coincident events as part of background. Estimate rate etc., from these events. GEO data analysis meeting, Golm

14. Detector Characterization • Instantaneous Detector “state” required • For estimating estimation errors and performance of transient tests via simulations. • Need to study estimation tools and fix what information constitutes detector state. • Characterization of rate and amplitude distribution of terrestrial bursts required for upper limit calculations. • This requires keeping track of detector history. • Analyze almost all data: Need an automated pipeline! GEO data analysis meeting, Golm