Using Machine Learning to Hunt for Simulated WIMPs in the NOvA Near Detector
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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.
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University of Minnesota M.S. thesis. November 2023. Major: Physics. Advisor: Alec Habig. 1 computer file (PDF); vii, 66 pages.
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Myers, Dalton. (2023). Using Machine Learning to Hunt for Simulated WIMPs in the NOvA Near Detector. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/260116.
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