Browsing by Subject "neutrinos"

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    Studies of Core-Collapse Supernova Models Using Past and Future Neutrino Data
    (2022-01) Olsen, Jackson
    Core-collapse supernovae expel a large amount of energy through the emission of neutrinos. These neutrinos carry important information about the environment in and around the core of the star during the first seconds after core bounce has occurred and the supernova shock has been initiated. By detecting the neutrino signal from a supernova, observers on Earth could extract this information from the neutrinos. Typically, simulations are used to accurately model the neutrino emission, with the neutrino signal depending on the input physics of the simulation. In this thesis, I consider the feasibility of using core-collapse supernova neutrino data to distinguish between different simulated supernova models, which vary in both the progenitor mass and the high-density nuclear equation of state. I also study whether SN neutrinos could be used to determine the neutrino mass hierarchy. Neutrinos have been observed from one supernova, SN 1987A. Unfortunately, the sample size is not very large. In the first part of this thesis, I describe a Bayesian analysis using the limited SN 1987A neutrino data from the Kamiokande II detector to compare several supernova models. This analysis indicates that the data most favors a model with a lower mass progenitor, and a shorter accretion emission phase. I also present the results of a secondary, goodness-of-fit analysis to test for incompatibility between the data and the models. The goodness-of-fit analysis suggests that the data is incompatible with the predicted total number of events from a model with a long, pronounced accretion period. The Bayesian analysis of the SN 1987A data does not provide us with comprehensive, definitive conclusions, given the limited size of the data set. In the second part of this thesis, I consider the case in which a future Galactic supernova occurs, and a water Cherenkov detector is operational to observe the neutrinos. With either an idealized detector model or a more realistic model based roughly on the Super-Kamiokande detector, the analysis indicates that if the supernova distance is known, all of the models compared could be distinguished in the case of a supernova distance of 25~kpc, and many for a supernova at 50~kpc. If the distance is unknown, the models could be distinguished at 10~kpc. In addition, assuming an idealized detector, three neutrino oscillation scenarios could be distinguished at 10~kpc. Therefore, the next Galactic core-collapse supernova is likely to provide information on its progenitor star, the high-density nuclear equation of state, and possibly the neutrino mass hierarchy.
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    Using Machine Learning to Hunt for Simulated WIMPs in the NOvA Near Detector
    (2023-11) Myers, Dalton
    A neural network was trained on simulated data that included events in which electrons were scattered by hypothetical Dark Matter particles (χ) of mass mχ = 30 MeV assuming a dark vector portal mechanism of a dark photon (A') with mass mA' = 90 MeV, a gauge coupling parameter αD = 1/2, and kinetic mixing parameter e = 2 × 10 -5. The NOvA Near Detector’s response to these events was then simulated, and the pixelmaps (images) of these events occurring within the NOvA Near Detector were then used to train a machine learning algorithm designed to differentiate between the each of the ordinary observed event types that involve an electron scattered by a neutrino and hypothetical events in which an electron was scattered by a dark matter particle.