Coherent network analysis technique for discriminating GW bursts from instrumental noise

1. Coherent network analysis technique for discriminating GW bursts from instrumental noise Patrick Sutton (CIT) in collaboration with Shourov Chatterji, Albert Lazzarini, Leo Stein (CIT),Antony Searle (ANU), Massimo Tinto (CIT/JPL)

2. Motivation • Expected sources of gravitational-wave bursts (GWBs) are usually poorly modelled. • Core-collapse supernovae, merging black holes • GW waveform not known a priori. • Coherent analysis: For a GWB, the signal in each detector should be correlated (same h+, hx). • This talk: Characterise one specific correlation test. • Real interferometers have populations of glitches, bursts of excess noise not due to gravitational waves. • How can we tell if a transient seen simultaneously in several detectors is a true GWB or an unlucky coincidence of glitches? Coherent network analysis technique for discriminating GW bursts from instrumental noise

3. s s “Null Streams” • Gürsel & Tinto, PRD 40, 3884 (1989). • Consider output of network of detectors at one time / frequency: antenna response (varies with sky position) detector noise spectrum GWB (unknown) detector noise (white) • GWB only couples through F+/s, Fx/s. • Project data orthogonally to F+/s, Fx/s. • True GWB will disappear, glitch will not! Coherent network analysis technique for discriminating GW bursts from instrumental noise

4. How much cancellation is enough? projection is function of F+,x(W), s(f) • Look at energy in projected data: auto-correlation terms “incoherent energy” cross-correlation terms “correlated energy” (gives cancellation) correlated energy correlation measure = incoherent energy Coherent network analysis technique for discriminating GW bursts from instrumental noise

5. Example: GWB vs. Glitch GWB has off- diagonal points (correlated energy) One point for each sky position tested. Glitch lies on diagonal (low correlation) GWB has off- diagonal points (high correlation) Glitch falls on diagonal Coherent network analysis technique for discriminating GW bursts from instrumental noise

6. Testing the method • GWBs: • 3 core-collapse supernova waveforms. • Dimmelmeier, Font, & Müller, A&A 393 523-542 (2002). • Pick one DFM and add to each detector data stream. • Glitches: • Inject a different supernova waveform into each detector • Use same time delays, amplitudes as a GWB. Pathological glitches! • Detector Network: • LIGO-Virgo network @ design sensitivity Coherent network analysis technique for discriminating GW bursts from instrumental noise

7. GWB and glitch populations clearly distinguished for SNR > 10-20. Similar to detection threshold in LIGO. Strongest Correlations for 104 GWBS & Glitches: • One point from each simulation. • sky position giving strongest correlation rrms = 100 50 Incoherent Energy 20 10 5 Null = Incoherent + Correlated Coherent network analysis technique for discriminating GW bursts from instrumental noise

8. Conclusions • Proposed coherent consistency test for distinguishing GWBs from noise. • Generalization of Gursel-Tinto null stream technique. • Compare correlated energy in null stream to uncorrelated energy. • Fractional correlated energy in null stream looks promising for discriminating GWBs from glitches. • Paper to gr-qc this week (Chatterji et al.). Coherent network analysis technique for discriminating GW bursts from instrumental noise

9. Supplemental Slides

10. Sky Maps: correlated / incoherent GWB Glitch source location no strong interference fringes (no correlations) strong interference fringes (correlations) Coherent network analysis technique for discriminating GW bursts from instrumental noise

11. Good discrimination for SNR > 10~20. Similar to detection threshold in LIGO. ROC: Distinguishing GWBs from Glitches Coherent network analysis technique for discriminating GW bursts from instrumental noise