发表机构
CERN（欧洲核子研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究团队提出可微中微子振荡引擎MANGO，其支持多种传播模型与新物理效应，可高效计算中微子振荡参数的精确梯度，在地球层析分析中展现出传统方法无法实现的实验设计评估能力。
AI 中文摘要
计算中微子振荡概率是已解决的问题，但其导数的计算尚未解决。我们提出MANGO（MANGO：中微子梯度振荡器），这是一种可组合的振荡引擎，其中每个计算量都相对于所有输入可微，包括传播几何、探测器深度和单个地幔壳密度，而这些量均未出现在传统的解析概率公式中。该引擎支持真空、恒定密度、层状PREM、任意剖面和绝热太阳传播，以及用于非标准相互作用、3+N惰性中微子态、退相干和非幺正混合的前端。由于反向模式的成本取决于输出维度而非输入维度，计算灵敏度会产生恒定的2.5至3倍的前向传递开销，因此计算层状地球的全部369个密度、电子分数和壳半径参数的灵敏度，所需成本不超过计算6个标准振荡参数的成本。通过维持精确的灵敏度信号，MANGO允许梯度连续流经概率阶段，并通过探测器响应、事件加权、分箱和似然函数。我们在一个风格化的地球层析分析中演示了这一点：MANGO单次计算出六区径向密度模型的边缘化不确定性，并通过对逆费舍尔矩阵求导，评估其对探测器角分辨率的灵敏度，这提供了传统解析概率公式无法实现的实验设计度量。三味和层状地球概率与外部基准（OscProb、NuFast-Earth）的匹配度在10^-9至10^-5之间，同时所有前向模型、BSM极限和微分路径均通过精确解析解和有限差分法验证。
英文摘要
Computing neutrino oscillation probabilities is a solved problem; computing their derivatives is not. We present MANGO (MANGO: A Neutrino Gradient Oscillator), a composable oscillation engine in which every computed quantity is differentiable with respect to all inputs, including propagation geometry, detector depth, and individual Earth-shell densities. None of these quantities appear in traditional analytic probability formulas. The engine supports vacuum, constant-density, layered-PREM, arbitrary-profile, and adiabatic solar propagation, alongside front-ends for non-standard interactions, 3+N sterile states, decoherence, and non-unitary mixing. Because reverse-mode cost depends on output rather than input dimension, evaluating sensitivities incurs a constant 2.5-3x forward-pass overhead. As a result, calculating sensitivities for all 369 density, electron-fraction, and shell-radius parameters of a layered Earth costs no more than the 6 standard oscillation parameters. By maintaining exact sensitivity signals, MANGO allows gradients to flow continuously past the probability stage and through detector response, event weighting, binning, and likelihoods. We demonstrate this on a stylized Earth-tomography analysis. In a single pass, MANGO computes the marginalized uncertainty on a six-zone radial density model and, by differentiating through the inverse Fisher matrix, evaluates its sensitivity with respect to detector angular resolution. This provides an experimental-design metric unreachable using traditional analytic probability formulas. Three-flavor and layered-Earth probabilities match external benchmarks (OscProb, NuFast-Earth) to within 10^-9 to 10^-5, while all forward models, BSM limits, and differentiation paths are verified against exact analytic solutions and finite differences.
Comments15 pages, 6 figures. Added references to complementary differentiable frameworks and related BSM literature
