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Group meeting

Group meeting. March 8 th 2013 JP Gómez González. Extended sources. Goal  To consider sources which are spatially extended (wrt PSF). Motivation  Correctly account for the correct source extension will enhance our chance for discovery

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Group meeting

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  1. Group meeting March 8th 2013 JP GómezGonzález

  2. Extended sources Goal  To consider sources which are spatially extended (wrt PSF). Motivation  Correctly account for the correct source extension will enhance our chance for discovery Implementation  Convolute PSF with source extension: As we model our PSF as a 2D Gaussian the convolution results in a broader Gaussian*. * We assume Gaussian sources of extension

  3. Extended sources (Likelihood) We assume our PSF is a two dimensional Gaussian on the form:

  4. Extended sources (Gaussian test)

  5. Extended sources (Discover flux) 3sigmas discovery

  6. Extended sources (Discover flux) 5sigmas discovery

  7. Extended sources (Discover flux) 5sigmas discovery

  8. Intensity photon maps

  9. Intensity photon maps

  10. Flux models Reference paper: Kappes et al. (2007b)

  11. Flux models • Procedure: • Simulate signal neutrinos with next energy spectrum (i.e. according to each model): • To create 3D-Histogram with (ψ,σ,ε);Angular resolution, Error estimate, Energy • Convolute PSF with morphology maps

  12. Flux models Neutrino flux models and model dependent upper limit:

  13. Flux models Use optimal discovery source extension ?

  14. Flux models Use optimal discovery source extension ?

  15. Flux models

  16. Upperlimits Calculate upper limit using pex where the source extension which maximises the discovery power is used in the likelihood… Problems at positive declinations 

  17. Upperlimits Feldman-Cousins and first bin in the only background test statistic distribution.

  18. Upperlimits Feldman-Cousins and first bin in the only background test statistic distribution

  19. New macros foranalysis

  20. Time calibration Goal  Calibrate Oms (ARSs) using muon time residuals. Idea  Use all (in opposite to unbiased*) hit time residuals distributions to calculate OM offsets. Motivation  The processing time can be reduced by one order of magnitude. * These are, for inter-line offsete calculation, the so-called probe hits.

  21. Time calibration How do these distributions look like?

  22. Time calibration How do these distributions look like?

  23. Time calibration Test: Add offsets to hit times (for each Omid), reconstruct tracks, and then try to calibrate back the Oms by finding the time offstes we have introduced by fitting the time residuals distributions with a Gaussian function.

  24. Time calibration Results does not reflect the offsets we have introduced

  25. Time calibration Lambda distributions worsen by intruducing the offsets

  26. TVC calibration Macros produced and more than 20 runs processed to extract TVC parameters.

  27. TVC calibration Evolution of the TVC distributions (mean value) per line

  28. TVC calibration All ARS (all runs) TVC distributions

  29. Extended sources (wrtobserv)

  30. Flux models

  31. Flux models Fitting with 2D Gaussian

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