将非弹性重构纳入IceCube中中微子质量顺序研究
Incorporating Inelasticity Reconstruction into Neutrino Mass Ordering Studies with IceCube
AI总结：
研究通过非弹性重构区分中微子与反中微子，提升IceCube探测中微子质量顺序的灵敏度。
AI中文摘要：
地球物质对大气中微子和反中微子振荡的影响取决于中微子质量顺序（NMO）。由于IceCube探测器预计探测到更多中微子而非反中微子，这种物质效应可用于探测NMO。中微子与反中微子相互作用时传递给核子的能量分数（即非弹性）因相反的旋性而有不同的分布。这在理论上可用于统计区分中微子与反中微子，但尚未在IceCube DeepCore分析中被利用。为此，开发了两种新的非弹性重构方法，分别使用图神经网络和两维卷积神经网络的集成。本报告讨论了这些重构算法的开发与性能。非弹性随后作为第四个可观测变量，与粒子能量、方向和味一起，计算新的NMO灵敏度，并确定在IceCube DeepCore和即将来临的IceCube升级探测器中测量NMO时加入非弹性的影响。
英文摘要：
Earth's matter affects the oscillation of atmospheric neutrinos and antineutrinos differently depending on the neutrino mass ordering (NMO). As more neutrinos than antineutrinos are expected to be detected in the IceCube detector, this matter effect can be used to probe the NMO. The fraction of energy transferred to the nucleon during a neutrino interaction, known as the inelasticity, has a different distribution for neutrinos and antineutrinos because of their opposite chirality. This can in theory be used to statistically separate neutrinos from antineutrinos, but hasn't been exploited in IceCube DeepCore analyses yet. To this end, two new inelasticity reconstructions were developed using a graph neural network and an ensemble of two-dimensional convolutional neural networks. This presentation discusses the development and performances of these reconstruction algorithms. The inelasticity is then used as a fourth observable, along with the particle energy, direction and flavor, to calculate new NMO sensitivities and determine the impact of adding the inelasticity in the measurement of the NMO with the IceCube DeepCore and upcoming IceCube Upgrade detectors.
