Hierarchical Search in Expanded Parameter Space

1. FDS: parameter space searches B. Allen, Y. Itoh, M.A. Papa, R. Prix, X. Siemens LIGO-G040117-00-Z Nov LSC meeting • Moving towards a hierarchical search. We now expand the coherent search to inspect a larger parameter space. (At the same time the incoherent stage is being developed and tested). • We will pursue two types of searches: • short observation time (~1/2 day), no spin-down params, wide band (~300 Hz) centered at ~ 300 Hz, all sky search • perhaps longer observation time, 1 spin-down param, small area search (galactic plane/SNRs), small bands. There is a delicate trade-off between sensitivity, observation time (spanned and effective), resolution in parameter space and class of sources that one chooses to target. Calibration info needed to finalize choice  SC03 demo. 1000CPUs across the grid for ~ 1 month. Note: different choices could be made in order to produce the best ULs. • entire S2 observation time, wide frequency band (200 Hz), in the direction of the galactic center. 1000 CPUs across the LSC grid for ~ 1 month: • AEI (Merlin, 360 CPUs), Birmingham (Tsunami, 200 CPUs), • Caltech (200 CPUs), Cardiff (120 CPUs), ISI (35 CPUs), • UWM (Medusa, 300 CPUs) SC03 failed results delayed due to failed scaling of grid tools with the size of the task. “Fix-up” restructuring of organization of pipeline - T. Creighton. LSC meeting, Hannover, Aug 2003

2. Selected band and observation times L1 : 732489168  732533276

3. Selected band and observation times H1 : 733803157  733837357

4. Selected band and observation times H1 : 733803157  733837357 L1 : 732489168  732533276

5. From S1 to S2 search • much larger parameter space: 105 sky positions, 300 Hz • template placement (did not use metric) • robust (automatic) noise estimation method (running median) • automatic identification of events (cluster identification) • vetoe method to reject large outliers • up-front “cleaning” of SFTs, where safe • develop a pipeline that will allow a detection, if a strong enough signal is present or set an upper limit. Aug LSC, debugged further, final at Nov LSC meeting Aug LSC, debugged further, final now GWDAW, further testing, final now. Y. Itoh. Developed after GWDAW. B. Krishnan. Pipeline has changed and still under development.

6. Sky position templates

7. Chi-square test on candidate events from CW signals coherent searches • A large value of the detection statistic indicates a candidate signal at the frequency and the sky position . • E.g., ~100 outliers of F(f) found in 100-500 Hz. • True signals produce characteristic line shapes in F=F(f). • Use the F(f) shape information to veto the outliers. • Left figure: Outlier example • Blue line: F(f) computed from LHO 10 h S2 real data targeting at . • Red line: Reconstructed veto signal based on the maximum likelihood estimates of the signal parameters.

8. Safety test False dismissal • Procedure • Inject ~ 10^6 fake signals with randomly chosen signal params and signal sky positions into 10 h Gaussian stationary noise. • Compute (~SNR) and the veto statistic for each outlier. Targeting sky position satisfies The red line gives 0 % false dismissal for this experiment.

9. Efficiency test False alarm • Procedure • Inject ~ 10^6 damped line noises with randomly chosen params into 10 hGaussian stationary noise. • Compute (~SNR) and the veto statistic for each outlier. Targeting sky position satisfies The red line gives 8 % false alarm for this experiment.

10. Fig.: SNR vs reduced chi. 1e6 M.C. experiments. Each blue dot corresponds to an injected signal into real data. Red line: 0% false dismissal, green line: 1.2% false dismissal.

11. Application to Real data • Procedure • Apply the veto method on 10 hLHO4K S2 real data starting from GPS time 731210229. • Compute (~SNR) and the veto statistic for each outlier. • 1200 targeting sky positions are randomly chosen. • Frequency band is 100-500 Hz. 465.7 Hz line 16 Hz multiples The red line vetos 69 % of the outliers found in this data. Some of the “Arms” are due to lines with particular freqs.

12. Pipeline

13. New pipeline using data of 2 detectors • this pipeline treats separately the data from each detector, thus does not exploit at best the noise rejection capability that stems from demanding consistency in 2 or more independent observations. • since we are not able to remove all outliers this point becomes particularly important in wanting to set an upper limit based on the loudest event. • clearly this is also important for detection, since a detection in coincidence increases the significance of an event and thus the final sensitivity of the search. • the most obvious modification to this pipeline is a triggered search.

15. consistent with expectations. h095% ~ few 10-23 2Fmax values in 0.5 Hz bands • 2Fmax value in a 0.5 Hz and searching ~ 15*15 deg around GC, 10h (H1 data) • after c2 test these most of these values will become smaller • from each of these values an h095% UL will be derived

16. Conclusion • all the pieces are in place. We are working on the scripts that actually implement the pipeline automatically. • once the pipeline is finally set, some tuning will be necessary to define some thresholds • when no candidate event is found to follow-up, an upper limit will be placed with software injections of a population of signals • draft + results expected @ June LSC meet