1. 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. FDS: parameter space searches B. Allen, Y. Itoh, M.A. Papa, X. Siemens AEI, UWM LSC meeting, Hannover, Aug 2003  entire S2 observation time, wide frequency band (200 Hz), in the vicinity of the galactic center. 1000 CPUs across the LSC grid for ~ 1 month: www.lsc-group.phys.uwm.edu/lscdatagrid/details.html AEI (Merlin, 360 CPUs), Birmingham (Tsunami, 200 CPUs), Caltech (200 CPUs), Cardiff (120 CPUs), ISI (35 CPUs), UWM (Medusa, 300 CPUs)

2. Modifications wrt the S1 analysis inserted loop to search over different sky locations and spin-down parameters introduced more robust S n estimation technique, based on running median (running median code by S. Mohanty) LSC meeting, Hannover, Aug 2003 found the bias correction factor for the expectation value of a running median from an exponential distribution as a function of window size (B. Krishnan)

3. Outlier due to large disturbance

4. Large outliers The good news: this, like all of the large outliers that we have seen, does not have the F(f0) shape that one would expect from a real signal. The half-height width from a signal is no more than ~ 7 bins wide and the peak is very sharp – no structure like this. So we will implement a test (chi-square test) to discard large outliers based on this principle.

5. Loudest event Assuming that there are no events that are statistically significant, we will proceed to set an upper limit. One can always do so, but if we thought that we may have an interesting event, we’d want to do follow-up studies. The larger the loudest event is, the less constraining our UL will be. This is one of the reasons why it is important to discard outliers, if possible. The procedure to set the UL given the loudest event will be the same as for S1: the detection efficiency will be measured by injecting signals having parameters that span the volume that one is searching.

6. Large outliers Identify large outlier clusters Test whether they can be discarded f 0 max  N  2F max For every significant cluster that is identified a line is written to a file (eventually to a DB): threshold  f0

7. Pipeline

8. Fstat shape test example

9. First results of  2 test (Y. Itoh)

10. Pipeline

11. 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  2 test these most of these values will become smaller from each of these values an h 0 95% UL will be derived consistent with expectations. h 0 95% ~ few 10 -23

12. 262-264 Hz, 28 sky locations, 10 hours, H1 data, entire pipeline 2F* (loudest event) = 37.9 run ~2E6 montecarlo injections in that band at a fixed value of h0, for different sky positions in the north emisphere and random values of  and cos( 

13. Schedule We could not produce complete the analysis due to severe failures of our computing facilities in the past month. Expect to have these within the next month – at GWDAW we would like to present methods rather than preliminary results.