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

6. MC samples Neutrino events: 50 x 106 (=-2) CORSIKA: 1000 x 106
(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 390 (=-1)
dataset 437 (=-2)
With =-2, we avoid to generate too many useless high energy events

8. Generated / expected Flatter ratio between generated and expected
dataset 390 (=-1)
dataset 437 (=-2)
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