论文标题
基于深度学习的基于中微子方向和能量的冰冰探测器的重建
Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors
论文作者
论文摘要
可以使用In-In-In-In-Radio检测进行成本效率测量超高能量（UHE）中微子（$> 10^{16} $ eV），这已在Pilot阵列中成功探索。目前正在格陵兰岛建造一个大型无线电检测器，具有测量第一个UHE中微子的潜力，并且使用IceCube-gen2计划了更敏感的检测器。对于这种浅的无线电探测器站，我们使用对模拟数据开发和测试的深神经网络（DNNS）对中微子能量和方向进行了端到端重建。 DNN在真实能量周围的标准偏差（$σ\ $ 0.3 in $ \ log_ {10}（e）$）周围确定了两个因子，该能量符合UHE中微子检测器的科学要求。我们第一次能够准确地预测所有事件拓扑的中微子方向，包括复杂的电子中微子充电电流（$ν_e$ -cc）相互作用。获得的角分辨率显示出$ \ Mathcal {O} $（$ 1^\ circ）$的狭窄峰，并带有扩展的尾巴，将68 \％的分位数推向非 - $ν_e$ -cc（spect。usp。$ν_e$ -cc互动）至$ 4^\ circ（5^\ circ）$。这突出了DNN在对无线电检测器数据中的复杂相关性进行建模的优势，从而实现了中微子能量和方向的测量。
Ultra-high-energy (UHE) neutrinos ($>10^{16}$ eV) can be measured cost-effectively using in-ice radio detection, which has been explored successfully in pilot arrays. A large radio detector is currently being constructed in Greenland with the potential to measure the first UHE neutrino, and an order-of-magnitude more sensitive detector is being planned with IceCube-Gen2. For such shallow radio detector stations, we present an end-to-end reconstruction of the neutrino energy and direction using deep neural networks (DNNs) developed and tested on simulated data. The DNN determines the energy with a standard deviation of a factor of two around the true energy ($σ\approx$ 0.3 in $\log_{10}(E)$), which meets the science requirements of UHE neutrino detectors. For the first time, we are able to predict the neutrino direction precisely for all event topologies including the complicated electron neutrino charged-current ($ν_e$-CC) interactions. The obtained angular resolution shows a narrow peak at $\mathcal{O}$($1^\circ)$ with extended tails that push the 68\% quantile for non-$ν_e$-CC (resp. $ν_e$-CC interactions) to $4^\circ (5^\circ)$. This highlights the advantages of DNNs for modeling the complex correlations in radio detector data, thereby enabling measurement of neutrino energy and direction.
