Coherent GRB Search in the WSR1 Data with X-Pipeline

1. Coherent GRB Search in the WSR1 Data with X-Pipeline Patrick Sutton & Michal Was LIGO-Caltech Ecole Normale Superieure

2. Outline • Coherent Statistics for GWB Detection • geometric interpretation • detection statistics • consistency tests • X-Pipeline & Triggered Searches • Triggered GRB search in the project IIB / WSR1 data set • analysis procedure • trigger characteristics • “loudest event” upper limits • compare LIGO-Virgo to LIGO-only Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

3. Coherent Analysis: Basic Formalism • Implicit time shift for sky position W. • GWB parameters: sky position W(q,f) (known for GRB case) and amplitudes h+(f), hx(f). • GWB can only appear in F+, Fx directions. • Form vector of whitened data from one frequency bin: Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

4. d3 d2 d1 Geometric View: One TF Pixel Work in “dominant polarization” frame (Klimenko et al, PRD ‘05): (noise only) (signal plus noise) (signal plus noise) Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

5. d3 d2 d1 Likelihood or Energy Measures Likelihoods used in this analysis: Hard Constraint Null Energy Incoherent Energy Klimenko, Mohanty, Rakhmanov,& Mitselmakher, PRD 72 122002 (2005); J. Phys. Conf. Ser. 32 12 (2006) Chatterji, Lazzarini, Stein, Sutton, Searle, & Tinto, PRD 74 082005 (2006) Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

6. X Pipeline • MatLab – based coherent analysis package. • http://www.ligo.caltech.edu/~psutton/protected/xpipeline/xpipeline.html • Code available in matapps CVS. • In GWB detection mode: • Time shifts data, FFTs, constructs time-freq. likelihood maps. • Chooses sky position that maximizes summed likelihood in each time slice. • Scripted for easy analysis of GRBs: ./grb.py --params-file grb.ini --grb-time 841896355 --right-ascension 217.5 --declination -28.8 --detector H1 --detector L1 --detector H2 --detector V1 Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

7. WSR1 GRB Search • No GRBs during WSR1 coincident operation … • There was a GRB a few minutes before the official start, but Virgo was not operating in science mode until about 10 hours later. • … therefore make up a GRB to serve as a test case: GPS trigger time = 841896355 right ascension = 217.5255 declination = -28.7510 • This was in the middle of the longest 5x (H1-H2-L1-G1-V1) coincidence segment. The sky position was chosen to favor GEO & Virgo: Site F_+^2+F_x^2 H 0.2598 L 0.3364 V 0.8356 G 0.7691 Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

8. Analysis procedure: data • Follow procedure similar to that used to date in LIGO GRB searches (Leonor et al.) • Data sets: • on-source data: +/- 1 minute around GRB time • off-source data: All H1-H2-L1-V1 coincident data within +/-12 hours of GRB trigger for background estimation (about 16 hours total). • simulations: Add GWB signals to on-source data • no data quality flags applied yet • Divide data into overlapping pieces of duration 1/256 sec. • FFT each piece, compute likelihoods summed over frequency bins [512,1536] Hz (5 bins). Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

9. Analysis procedure: likelihoods • Analyse all data, using hard constraint likelihood as the detection statistic (Leonor uses cross-correlation). • Record all hard constraint likelihoods above some low threshold (~10 Hz false rate). • Compare null vs. incoherent energy as consistency test to remove glitches (Chatterji et al. PRD). • Use off-source and simulation results to tune the null vs. incoherent consistency test. Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

10. Simulated GWBs • Lazarus black-hole merger waveforms (Baker et al. ’02) • Equal-mass, non-spinning 5+5 Mo. • Circularly polarized (we’re looking down the GRB axis) • Not a real waveform for GRBs! Just a test waveform in-band. Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

11. Likelihood Distributions Events from off-source data Color-coded by hard constraint likelihood Glitches lies on diagonal loud glitches Gaussian background Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

12. Likelihood Distributions Lazarus GWBs Real GWB signals lie above the diagonal. hrss = 5e-22 / Hz1/2 hrss = 2.5e-22 / Hz1/2 loud glitches Gaussian background Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

13. Likelihood Distributions (Zoomed-in view) Threshold on ratio Einc/Enull to remove glitches. Optimum value (1.12) determined by computing expected upper limit for off-source data. Will try more sophisticated cuts in the future. loud glitches Gaussian background Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

14. Repeat entire analysis for H1-H2-L1 network Use exactly the same tuning procedure for Einc/Enull cut. LIGO-only 90% Upper Limit: hrss = 4.6 x 10-22 Hz-1/2 Using Virgo data improved amplitude sensitivity! GRB position favored Virgo by a factor ~2.5. Practice Upper Limit: Look at on-source data. Find largest hard constraint likelihood surviving Einc/Enull cut (``loudest event’’). Upper limit on GWB amplitude is the smallest amplitude such that 90% of injections survive Einc/Enull cut and are louder than loudest event. LIGO-Virgo 90% Upper Limit: hrss = 3.7 x 10-22 Hz-1/2 On-Source Results Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

15. Summary • Used X-Pipeline to demonstrate a preliminary, but complete, fully coherent triggered analysis. • From raw strain data to loudest event upper limit. • Coherent statistics for detection and glitch rejection. • Analysed 2 different network configurations (LIGO-Virgo & LIGO-only). • Combining Virgo WSR1 data with LIGO data gave an improved amplitude upper limit for a GRB favorably positioned for Virgo. • More study needed to determine scientific value of LIGO-Virgo joint analyses • Used atypical GRB, but Virgo data improving since WSR1 … Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline

16. Future Plans • Finish script job processing infrastructure • mainly post-processing codes, combining multiple time-frequency resolutions, & automated tuning. • Finish implementation and testing of clustering algorithm for time-frequency maps • using J. Sylvestre’s “generalized clusters” algorithm (TFClusters) • improve sensitivity • Run over S5 GRB set Sutton: Coherent GRB Search in the WSR1 Data with X-Pipeline