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IC22 Unbinned GRB Search Utrecht Collaboration Meeting

IC22 Unbinned GRB Search Utrecht Collaboration Meeting. Erik Strahler UW-Madison 16/9/2008. IC22 Data Sample. 43/44 Northern hemisphere quality bursts Filtered to L3 Use complete year for background rejection Yields good statistics (~77M events) Bursts windows taken from the Swift T100s

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IC22 Unbinned GRB Search Utrecht Collaboration Meeting

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  1. IC22 Unbinned GRB SearchUtrecht Collaboration Meeting Erik Strahler UW-Madison 16/9/2008

  2. IC22 Data Sample • 43/44 Northern hemisphere quality bursts • Filtered to L3 • Use complete year for background rejection • Yields good statistics (~77M events) • Bursts windows taken from the Swift T100s • Conservative • Easier than trying to figure out T90 information + padding • Total ontime: 4961.3s • Blind runs containing GRB triggers

  3. Simulation • Corsika Dataset: 645 (0.3 days) • Corsika Coincident Dataset: 861 (0.71 days) • Neutrino-Generator Datasets: • 651 (E − 1, 1M) • 768 (E − 2, 5.5M) • Processed identically to data • Weighted to average WB prompt emission spectrum • Flux Normalization: 4.5E-9 GeV s-1 sr-1 cm-2 • To Do: look at more muon statistics for checking tails

  4. Cut Parameters

  5. Bayesian Ratio Reduced LLH vs. Ndir Split Reco MinZen

  6. L4 = L3 + paraboloid fit succeeded AND split reco succeeded AND neutrinos >= 90 degrees

  7. “Loose” Cuts

  8. Efficiency vs. Energy • ~50% at 100 TeV (peak of WB signal acceptance)

  9. Efficiency vs. NCH

  10. Effective Area

  11. Effective Area

  12. Bayesian ratio > 20, rllh < 8.25

  13. Likelihood Method • Similar Method to GRB080319B except with 43 stacked sources • Also incorporates energy as in the point source work • Currently using fixed E-2 signal. (WB makes little change) • Doesn’t make sense to fit the spectrum for 43 bursts and only 1 or 2 events • Perform algorithm while optimizing rllh and bayesian ratio Signal Spatial PDF x PDFiE signal atm. corsika signal

  14. Test Statistic 0 injected signal

  15. Optimized Cuts Reduced LLH Reduced LLH Bayesian LLH Ratio Bayesian LLH Ratio 3s, P=0.5 MDP optimization 4s, P=0.5 MDP optimization

  16. Discovery Potential • Energy does not help as much as in the point source search • ~15% improvement at P=0.5 for 4s

  17. Conclusions • Good event selection • Similar to point source search • Nearly at atm. neutrino level • Good signal retention (0.7 events expected) • Implemented likelihood function incorporating position, time, and energy (as nch) • Initial tests show good discovery potential • To Do: • Data stabililty • Run statistics for 5s numbers • Implement individual GRB flux expectations

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