标题： 基于深度学习的IceCube-Gen2射频探测器事件重建 显示英文标题

标题： Deep Learning Based Event Reconstruction for the IceCube-Gen2 Radio Detector

摘要： 南极点的IceCube-Gen2地下冰无线电阵列计划将为EeV范围内的超高能（UHE）中微子提供前所未有的灵敏度。 探测器测量中微子能量和方向的能力至关重要。 本贡献提出了使用深度神经网络（DNNs）对混合无线电阵列（“浅层”和“深层”）两种探测器组件的这两个量进行端到端重建。 我们能够精确预测所有事件拓扑的中微子方向和能量，包括由于LPM效应而更为复杂的电子中微子电荷流相互作用。 这突显了DNNs在建模无线电探测器数据中复杂相关性的优势，从而实现了对中微子能量和方向的测量。 我们讨论了如何利用归一化流来预测每个单独事件的PDF，从而建模重构中微子方向的复杂非高斯不确定性轮廓。 最后，我们讨论了这项工作如何用于进一步优化探测器布局以提高其重构性能。 显示英文摘要

摘要： The planned in-ice radio array of IceCube-Gen2 at the South Pole will provide unprecedented sensitivity to ultra-high-energy (UHE) neutrinos in the EeV range. The ability of the detector to measure the neutrino's energy and direction is of crucial importance. This contribution presents an end-to-end reconstruction of both of these quantities for both detector components of the hybrid radio array ('shallow' and 'deep') using deep neural networks (DNNs). We are able to predict the neutrino's direction and energy precisely for all event topologies, including the electron neutrino charged-current interactions, which are more complex due to the LPM effect. This highlights the advantages of DNNs for modeling the complex correlations in radio detector data, thereby enabling a measurement of the neutrino energy and direction. We discuss how we can use normalizing flows to predict the PDF for each individual event which allows modeling the complex non-Gaussian uncertainty contours of the reconstructed neutrino direction. Finally, we discuss how this work can be used to further optimize the detector layout to improve its reconstruction performance.