Echoes of the Cosmic Collapse: Unraveling Neutrino Mysteries with Supernovae
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Neutrinos are among the most abundant particles in the Universe. They hold the key to unlocking profound insights across particle physics, astrophysics, and cosmology. Despite their prevalence, our understanding of their properties and the astrophysical environments in which they are produced remains limited due to their weak interactions with other particles. Fortunately, core-collapse supernovae (CCSNe), the calamitous explosions that occur when the cores of massive stars collapse after they run out of nuclear fuel, produce colossal amounts of neutrinos. The observed electron antineutrinos from SN 1987A, a nearby CCSN, have provided valuable information about the nature of neutrinos and, for the first time, allowed us to probe the most compact regions in CCSNe. Modern neutrino experiments will detect supernova neutrinos with unprecedented statistics when the next galactic CCSN happens. This highlights the importance of studying supernova neutrinos. However, robust theoretical frameworks for macroscopic astrophysics and microscopic neutrino interactions, detailed analyses of observational data, and reduction of experimental background are essential to decoding the unique information brought by supernova neutrinos. In this dissertation, I explore supernova neutrinos and their implications for particle physics and astrophysics, which comprises four main parts that align with all the aforementioned aspects: (1) I review the CCSNe mechanisms, supernova neutrino production, the SN 1987A neutrino observations, and the outlook for the future. (2) I develop theoretical frameworks to test nonstandard neutrino self-interactions using supernova neutrino data. (3) I conduct an extensive search for supernovae as potential sources of the high-energy astrophysical neutrino events observed by IceCube. (4) I apply deep neural networks to identify the muon-induced background in Super-Kamiokande, one of the major experiments for supernova neutrinos. Following the context, I present my independent research on predicting the strong gravitational lens parameters with state-of-the-art deep learning architectures.Combined, the series of my work aims to open up various avenues to unravel the neutrino mysteries with supernovae.References(428)
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