Material Sciences - Doctoral Theses

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Browsing Material Sciences - Doctoral Theses by Author "Rumleskie, Janet"

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    SNO+ sensitivities to pre-supernova and supernova neutrinos
    (2021-09-10) Rumleskie, Janet
    Core-collapse supernovae (CCSNe) deposit heavy elements into the surrounding interstellar medium, affect the chemical evolution of galaxies, and lead to the formation of exotic bodies such as neutron stars and black holes. Neutrinos are also generated deep in the core of the CCSN over the timescale of tens of seconds and possess an imprint of the unconfirmed core-collapse mechanism. The progenitor stars to CCSNe also emit neutrinos in the final hours prior to collapse, albeit with an order of magnitude lower intensity and energy. A pre-supernova monitor and alert system was developed for the SNO+ detector based on inverse beta decay events. Such an alert will warn SNO+ and other neutrino experiments to maintain uptime to detect neutrinos from the impending supernova. Assuming the bestcase progenitor model, neutrino hierarchy, and background rates, the pre-supernova alert is expected to alarm for candidates as early as 100 hours prior to a supernova and up to a distance of 640 pc. Details of the construction and sensitivity of the pre-supernova monitor are presented within this thesis. The event rate from the next galactic supernova in SNO+ will depend on distance, but could exceed typical detector operating rates by an order of magnitude. The SNO+ data acquisition was stress-tested in this thesis with an embedded light injection source and compared to simulations characterizing the SNO+ detector’s response to CCSNe. These tests revealed SNO+ will experience data loss for any CCSN closer than 1.4 kpc. In addition, the SNO+ detector is expected to collect data from the neutronization burst of Betelgeuse before the data acquisition is overwhelmed. Using the detector simulations, a programmable laser diode was also prepared to emulate the light expected in SNO+ from supernova neutrinos. This light source is more dynamic and isotropic than the embedded source and will eliminate many of the uncertainties from the embedded source. It will be deployed in the future.