利用卷积神经网络结合 IceCube DeepCore 9.3 年数据测量大气中微子振荡参数
Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore
浏览论文内容
中文总结 AI 辅助
本研究利用 IceCube DeepCore 9.3 年大气中微子数据，结合卷积神经网络高级重建方法，以更高统计量和纯度精确测量了中微子振荡参数 Δm²₃₂ 和 sin²θ₂₃，获得了目前大气中微子最精确的结果。
中文摘要 AI 辅助
IceCube 中微子观测站的 DeepCore 子探测器能够探测能量约 5 GeV 以上的中微子。本研究利用 2012 至 2021 年间（共 3,387 天）采集的数据，开展大气 νμ 消失分析，研究了 150,257 个重建能量在 5–100 GeV 之间的中微子候选事例。分析中应用了一种基于卷积神经网络的高级重建方法，提高了信号效率和本底抑制能力，使得该测量与以往 DeepCore 振荡结果相比统计量显著增加，同时具有较高的中微子纯度。在正的中微子质量顺序下，测得大气中微子振荡参数及其 1σ 误差为 Δm²₃₂ = 2.40⁺⁰·⁰⁵₋₀·₀₄ × 10⁻³ eV² 和 sin²θ₂₃ = 0.54⁺⁰·⁰⁴₋₀·₀₃。该结果是目前利用大气中微子获得的最精确结果，并且与包括长基线加速器实验在内的其他中微子探测器的测量结果相兼容。
英文摘要
The DeepCore sub-detector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV. Data taken between 2012-2021 (3,387 days) are utilized for an atmospheric $ν_μ$ disappearance analysis that studied 150,257 neutrino-candidate events with reconstructed energies between 5-100 GeV. An advanced reconstruction based on a convolutional neural network is applied, providing increased signal efficiency and background suppression, resulting in a measurement with both significantly increased statistics compared to previous DeepCore oscillation results and high neutrino purity. For the normal neutrino mass ordering, the atmospheric neutrino oscillation parameters and their 1$σ$ errors are measured to be $Δ$m$^2_{32}$ = $2.40\substack{+0.05 \\ -0.04} \times 10^{-3} \textrm{ eV}^2$ and sin$^2$$θ_{23}$=$0.54\substack{+0.04 \\ -0.03}$. The results are the most precise to date using atmospheric neutrinos, and are compatible with measurements from other neutrino detectors including long-baseline accelerator experiments.
