1. IC40 Spectrum Unfolding 7/1/2016Warren Huelsnitz1 SVD Method described in A. Höcker and V. Kartvelishvili, NIM A 372 (1996) 469NIM A 372 (1996) 469 Implemented in RooUnfold: http://hepunx.rl.ac.uk/~adye/software/unfold/RooUnfold.htmlhttp://hepunx.rl.ac.uk/~adye/software/unfold/RooUnfold.html Used only 97-180 zenith region in unfolding: 17689 events from IC40 data set Observable was the reconstructed muon dE/dX Honda + Sarcevic flux models (zenith averaged) Unfolded spectrum (zenith averaged) Observable

2. Statistical Errors Unfolding with different regularization parameters Unfolding different zenith regions “Horizontal” (97 to 124 degrees) “Upgoing” (124 to 180 degrees) Uncertainties in Unfolding Six major sources of uncertainty:  Homogenous uncertainties in photon flux or detection due to uncertainties in OM sensitivity and ice properties  Zenith-dependent uncertainties (data/simulation mismatch, atmospheric variability)  Regularization uncertainties (choice of “kterm”; amount of smoothing)  Statistical uncertainties (from toy MC tests)  Errors due to event weighting in simulation  Miscellaneous normalization errors (assumed uniform 4%): cross-sections, muon energy loss, background, etc. Range of uncertainty due to OM/Ice Honda + Sarcevic flux models Simulated event rate converted to atmospheric flux using OneWeight Event weighting errors

3.  Homogenous uncertainties (OM sensitivity and ice properties)  Zenith-dependent uncertainties (data/simulation mismatch, atmospheric variability)  Statistical uncertainties (from toy MC tests) Uncertainties in Unfolding 7/1/2016Warren Huelsnitz3  Miscellaneous normalization errors (cross- sections, muon energy loss, background, etc.)  Errors due to event weighting in simulation  Regularization uncertainties (choice of “kterm”; amount of smoothing)

4. 7/1/2016Warren Huelsnitz4

5. Comparisons to various flux models Need to reduce uncertainties through improved ice model, better light propagation in simulation, more statistics, accounting for seasonal and regional atmospheric variability, OM sensitivity measurements

6. Direction Dependent Oscillations 7/1/2016Warren Huelsnitz6 Standard Model Extension includes interaction coefficients that violate rotational invariance “Vector Model” Kostelecky and Mewes, PR D 70, 076002 Oscillation probability depends on neutrino energy and propagation length, also depends on X and Y components of neutrino direction Feedback from tau cycle accounted for in analysis; dampens oscillation signal by ~ 6 % for a, ~ 9 % for c

7. 7/1/2016Warren Huelsnitz7 Direction Dependent Oscillations: DFT Analysis Discrete Fourier Transform of Data –Start with data; randomize RA’s and re-compute power spectral density (PSD) 10000 times; determines spread of PSDs due to “noise” –Data is consistent with no sidereal signal (98% of random samples had greater PSD in n=4 mode) Determine limits on SME coefficients –400 MC experiments –Dial up SME coefficients (one at a time) until PSD in n=4 mode exceeds 99.87% (3-sigma) of “noise” PSDs –Take mean of 400 determinations Systematic uncertainties in cosmic ray spectral index, DOM sensitivity, and ice properties, lead to uncertainty in the neutrino energy distribution represented by the data 32 bins in RA (using zenith 97 to 120)