Measuring the atmospheric neutrino oscillation parameters with icecube deepcore
Aug 24, 202290 pages
Supervisor:
Thesis: PhD - U. Wisconsin, Madison (main),
- University of Wisconsin Madison
- Published: 2022
Experiments:
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Abstract: (U. Wisconsin, Madison (main))
For decades, the Standard Model of Particle Physics has stood the test of time, being one of the most comprehensive and reliable models ever proposed. One of the few exceptions to its robustness was the discovery of neutrino oscillations (and consequently the implication that neutrinos have mass). It is one of the only confirmed pieces of evidence of physics beyond the Standard Model. Since this discovery, there has been a worldwide effort on both the theoretical and experimental fronts to answer many questions that this discovery raised. Many neutrino experiments around the world seek to measure the model parameters that describe these oscillations within the current theoretical 3 neutrino framework. The IceCube Neutrino Observatory is a kilometer-scale detector embedded in the Antarctic ice at the South Pole and detects Cherenkov light produced by neutrinos interacting in the ice. DeepCore is a densely instrumented sub-array within the full detector that observes interactions of atmospheric neutrinos down to 5 GeV. At these energies, Earth-crossing muon neutrinos have a high chance of oscillating to tau neutrinos. DeepCore is able to measure these oscillations with precision comparable to accelerator-based experiments, but it is also complementary to accelerator measurements because it probes longer distance scales and higher energies, peaking above the tau lepton production threshold. This dissertation presents the effort involved in curating one of the largest neutrino oscillation datasets in the world, with over 200,000 events spanning almost 10 years (from April 2012 to January 2022) and a neutrino purity of over 99%. This sample is optimized for performing a measurement of the atmospheric neutrino oscillation parameters. The nearly unprecedented level of statistics also requires unprecedented precision in the treatment of systematic uncertainties. Compared to previous DeepCore analyses, this analysis benefits from improved background rejection, new simulation, a new reconstruction, a new machine learning particle classification algorithm, improved modeling of systematic uncertainties, and more years of data. Given the large number of changes compared to previous analyses, an extensive suite of quality and robustness checks were performed. Even with the extensive testing program, our fit to data informed us that our modeling is not yet on par with the level of statistical precision achieved with the new sample; the goodness-of-fit between our data and simulation yielded a p-value of 0[percent]. At the time of writing, there remains an ongoing effort to improve our models to describe the full dataset. This dissertation includes the unblinded results of an analysis performed with a sub-sample containing 20[percent] of the full dataset.- Particle physics
- Physics
- Atmospheric neutrinos
- IceCube
- Neutrino oscillations
- Neutrinos
- Particle astrophysics
- neutrino: oscillation
- atmosphere
- radiation: Cherenkov
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