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Sterile Neutrino Searches at the IceCube Neutrino Observatory

High Energy Physics - Experiment 2020-05-05 v1 Instrumentation and Detectors

Abstract

The IceCube Neutrino Observatory is capable of performing a unique search for sterile neutrinos through the exploitation of a matter enhanced resonant neutrino oscillation phenomena. As atmospheric muon neutrinos pass the dense material within the Earth, neutral current elastic forward scattering is predicted to induce a transition into a sterile state. This thesis presents two 3+1 sterile neutrino analyses by searching for spectral differences in the reconstructed energy and zenith direction of muon neutrino events, indicative of a transition into a sterile state. The first search probes the parameter space Δ\Deltam412^2_{41} and sin2^2(2θ24\theta_{24}) with relevant sensitivity to the global best fit region for a 3+1 sterile neutrino hypothesis. The second search performs a scan through sin2^2(2θ24\theta_{24}) and sin2^2(2θ34\theta_{34}) in the oscillation averaged out region of high-Δ\Deltam412^2_{41} (Δ\Deltam412^2_{41} \gtrsim 10eV2^2). The analyses are performed using an improved event selection, which was found to extract 305,891 well reconstructed muon neutrino events with a sample purity above 99.9\%, from eight years of IceCube data. Novel simulation techniques, along with updated calibration, and a re-assessment of the systematic uncertainties are also discussed. The first analysis finds a best fit sterile hypothesis point at Δ\Deltam412^2_{41} = 4.47eV2^2 and sin2^2(θ24)\theta_{24}) =0.10, consistent with the no-sterile hypothesis at the 8\% confidence level. The second analysis finds a best fit sterile hypothesis at sin2^2(θ34)\theta_{34}) = 0.40, sin2^2(θ24)\theta_{24}) = 0.006, consistent with the null hypothesis at the 19\% confidence level. % and provides a significant improvement in the worlds measurements using muon neutrino disappearance.

Keywords

Cite

@article{arxiv.2003.02796,
  title  = {Sterile Neutrino Searches at the IceCube Neutrino Observatory},
  author = {Spencer Axani},
  journal= {arXiv preprint arXiv:2003.02796},
  year   = {2020}
}

Comments

PhD thesis, MIT (2020)