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Unfolding atmospheric neutrino spectrum with IC9 data (second update)

Unfolding atmospheric neutrino spectrum with IC9 data (second update). 4 / 4 / 2007. Upgrades. Use of the energy estimate of Rime Smearing matrix build with a distribution closer to what we expect to measure. Reminder: How to choose the best variable?. Criteria:

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Unfolding atmospheric neutrino spectrum with IC9 data (second update)

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  1. Unfolding atmospheric neutrino spectrum with IC9 data (second update) 4 / 4 / 2007

  2. Upgrades • Use of the energy estimate of Rime • Smearing matrix build with a distribution closer to what we expect to measure

  3. Reminder: How to choose the best variable? • Criteria: • As much linear as possible with energy • With the least spread • Possible candidates: nchan, tot_charge, energy estimator

  4. log10 of Total charge delta log10 Charge (pe) log10 Charge (pe) log10 Enu [GeV] log10 Enu [GeV] RMS 0.42 RMS is related with the quality of the fit deviation from fit / delta Y-projection log10 Enu [GeV]

  5. log10 of estimator log10 estimator log10 estimator log10 Nchan log10 Enu [GeV] log10 Enu [GeV] log10 Nchan deviation from fit / delta log10 Nchan log10 Enu [GeV]

  6. MC samples • Neutrino events: 50 x 106 (=-2) (dataset 437) • CORSIKA: 1000 x 106 (dataset 296) • Coincident muons: 3000 x 106 (dataset 394) (files had to be reprocess to include the energy estimator)

  7. Spectral index for simulation • It is convenient to generate the MC sample in such a way that generated/expected is flat (more efficient smearing matrix calculation, smoother distribution to unfold…) dataset 437 (=-2) dataset 390 (=-1) With =-2, we avoid to generate too many useless high energy events

  8. Generated / expected dataset 437 (=-2) dataset 390 (=-1) Flatter ratio between generated and expected Still, statistics could be not enough (factor 10 more)

  9. Processing • First guess with line fit • The result is used to feed the LLH • Quality cuts are applied after processing

  10. Ndir  8, Ldir > 200 m,  > 92 atmospheric neutrinos: 671 corsika events: 13 coincident muons: 2.6 purity: 98%

  11. Unfolding: initial distribution • In unfolding problems, it is usually very useful to have an idea of the distribution we want to unfold. • BUT the robustness of our method against our choice of the initial distribution has to be checked.

  12. Smearing matrix Maybe more statistics are still needed…

  13. Measured distribution (background from single and double atmospheric muons already included)

  14. Unfolded spectrum Using Singular Value Decomposition:

  15. Deviation from the true spectrum

  16. New initial spectrum • We have to make things harder to the algorithm, to check its robustness:

  17. The unfolded spectrum follows well the true spectrum A more systematic way of robustness check is underway

  18. Difference between true and unfolded

  19. Summary and Prospects • Energy estimate shows better behavior as energy correlated variable • The new MC (gamma=-2) offers flatter generate / expected distribution • Good quality of unfolding even when the initial spectrum is different from the true one • To do list: • More statistics for smearing matrix • Combine different variables (?) • Continue on optimization of cuts • Tuning of regularization constant and other internal parameters

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