Atmospheric neutrino energy spectrum with IceCube (22 strings)

1. Atmospheric neutrino energy spectrum with IceCube (22 strings) Dmitry Chirkin, University of Wisconsin at Madison, U.S.A., for the IceCube collaboration

2. Event selection New framework for applying cuts: SBM (subset browsing method), see my poster D.Chirkin “A new method for identifying neutrino events in IceCube data” (simulated ns and ms) atmospheric ns atmospheric ms 2290 events 4492 events 8548 events 95% ~90% 99% purity 2290 events 4492 events 8548 events 90 – 180o 90 – 120o 120 – 150o 150 – 180o 30 parameters identified to separate signal and background Step 1: constructs surface separating signal from background Step 2: additional requirements for similarity with simulated signal 275.5 days of IceCube (22 strings) taken in 2007

3. Muon energy resolution reconstructed muon energy distribution simulation data True (from simulation) muon energy distribution Precision of the energy measurement: reconstructed vs. simulated true: ~ 0.3 in log10(E)

4. Parameter distributions Reconstructed zenith angle distribution vertical up horizontal Center of gravity (COG), or “average” event depth data simulation data simulation 2400 2200 2000 1800 1600 center of gravity depth [m] Point-spread function (PSF): Median angular resolution is ~ 2o.

5. Neutrino energy from reconstructed muon energy What we have: muon energy at detector with 0.3 in log10(E) resolution and its zenith angle with ~1.5o resolution What we want: muon neutrino energy distribution The transformation matrix is known from the simulation and relates muon and neutrino numbers: m=An Transformation/unfolding matrix

6. Unfolding methods • Performance of the following unfolding methods was studied: • Simple inversion and no-regularization c2 and likelihood minimization • SVD (singular value decomposition): • regularizing with the 2nd derivative of the unfolded statistical weight • regularizing with the 2nd derivative of the unfolded log(flux) • This is the selected method as it has the best behavior for: constant spectral index  regularization term goes to 0 best identification of deviations from the given spectrum • also added the likelihood term describing fluctuations in the unfolding matrix • Bayesian iterative unfolding: • with and without smoothing of the unfolding matrix

7. Testing for bias, diffuse E-2 flux

8. Testing for spectral index, charm contribution rqpm

9. Errors from belt construction, ½-likelihood estimate From 1000 simulations For a single representative simulation

10. Including fluctuations of the smearing matrix preliminary cf. AMANDA-II 2000-3: ~ 1.2 99 38 1.9 0.5 15 0.1 4.6 2.1 0.3 Unfolded data For a single representative simulation

11. Unfolded data at 2 different quality levels preliminary preliminary

12. Unfolded data with only events in the top or bottom preliminary preliminary

13. Conclusions and Outlook preliminary • Despite some residual problems in detector simulation, agreement with Barr. et al. (Bartol) muon neutrino flux is demonstrated • Improving the simulation is actively pursued, and the result with reduced systematic (and smearing matrix statistical) uncertainties will be very interesting