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Analysis of 2000 EHE Events Using Iterative Reconstruction Method

This study investigates the unstable nature of iterative reconstruction in analyzing EHE events, impacting zenith angle results. The analysis focused on fluctuating events and proposed new cuts to mitigate the issue, enhancing the data resolution. The research highlights fluctuator characteristics and strategies to refine reconstruction methods for better event classification. Promising cuts at level 3 promise improved sensitivity without depending on iterative reconstruction. The outlook suggests ongoing improvements in the analysis to enhance data accuracy and efficiency.

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Analysis of 2000 EHE Events Using Iterative Reconstruction Method

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  1. Status of the 2000 EHE Analysis with AMANDA-II Lisa Gerhardt Berkeley, 2005

  2. Previously • Requested unblinding for EHE analysis for 2000 year with cuts detailed at http://www.ps.uci.edu/~gerhardt/uhe/uhe.html • Evidence turned up which suggested the iterative reconstruction may be unstable for some events • Stopped unblinding to investigate this

  3. Iterative Reconstruction • Iterative reconstruction refers to reconstruction which repeats the reconstruction a specified number of times (16) • Uses single Pandel reconstruction as a reference, each new iteration starts from a randomly generated track • Pandel, MPE and best downgoing fit • During the unblinding process, it was discovered that values for the iterative reconstruction fluctuated wildly • Zenith angle can change by as much as 40

  4. Number Passing Cuts Also Fluctuates • Repeated processing on 20% data sample • All steps the same • Previous had no events pass cuts, now 3 events pass • Only due to fluctuations of iterative reconstruction • Similar behavior observed in MC (both BG and signal) • Cuts are tuned to a particular fluctuation of the iterative reconstruction

  5. Increasing Iterations Doesn't Help Fluctuating between two values

  6. MC Fluctuator Characteristics • Fluctuators in BG MC tend to have slightly higher energy primaries and to be less vertical

  7. Data Fluctuator Characteristics • Spread throughout the data year and independent of number of modules hit

  8. Nice afterpulse peak (15%) Possible horizontal event with large energy deposit inside array

  9. 30% of hits Possible downgoing event

  10. Iterative Reco. Used Throughout Analysis • Level 0 – hit cleaning • Level 1– FRAC1, nhits • Level 2 – FRAC1 • Level 3 – energy(HE), likelihood(down), NN1 • Level 4 – zenith(HE), NN2 • Level 5 – nhits, nch, zenith(down), zenith(Pan) • Level 6 – avg. hit prob.(mpe), zenith(down) Red = likelihood reconstruction

  11. Strategy • Fall back to level 2 • Separate fluctuating events from non-fluctuating events • Apply cuts on previous slide to non-fluctuators • Can relax some of the cut values • Develop new cuts for fluctuators which do not use iterative reconstruction • Hit topology, first guess reconstruction • Fluctuation must be correctly modeled in BG MC

  12. Strategy • Events are considered fluctuators if reconstruction iterated 16, 32, 64 and 128 times disagrees by more than 2 degrees • Check Pandel, MPE and downgoing reconstructions, if any one disagrees, event is a fluctuator • Too computationally intensive to start at level 2, so use level 3 as a test of principle while level 2 is processing • 1.4 x 105 events at level 2 versus 2300 at level 3, BG MC takes ~45 s per event, signal takes ~60 s

  13. Passing Rates • Data (L3): 3100 (34%) non-fluctuators 6068 (66%) fluctuators • BG MC (L3): 2139 (28%) non-fluctuators 5612 (72%) fluctuators • Signal (L3): E-2 17% non-fluctutators E-2 83% fluctuators BG MC and data are in good agreement

  14. New Cuts Promising cuts that don't depend on the iterative reconstruction have been found

  15. Outlook • Cuts have been found at level 3 which leave roughly the same sensitivity (~15% worse than old analysis) • Improvement can continue while level 2 is processing • Level 2 processing should be done in a few weeks • Expect new unblinding request then

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