ADoNIS：中微子相互作用样本的可微分生成器
ADoNIS: A Differentiable generatOr of Neutrino Interaction Samples
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中文总结 AI 辅助
本研究提出可微分中微子相互作用事例发生器ADoNIS，其可直接提供事例重加权及其梯度，在保持物理保真度的同时适用于多类任务，为中微子振荡分析提供实用工具。
中文摘要 AI 辅助
中微子相互作用发生器是精确振荡分析的核心，但传统实现无法直接提供其预测对物理参数的导数，使得分析需通过重加权、有限差分或外部响应模型获取这种依赖关系。我们提出ADoNIS，一种完全可微分的中微子相互作用事例发生器，可直接提供事例重加权及其梯度，将精确导数传播至核基态、硬散射振幅和随机核内级联。遵循ACHILLES的物理选择，我们展示其在中微子、电子和强子探针的预测与ACHILLES一致，表明实现可微分性的同时未损失物理保真度。随后我们展示所得雅可比矩阵如何揭示哪些测量和相空间区域约束每个参数，以及不同探针如何打破简并，为拟合设计、模型调优、测量设计和实验设计提供定量工具。相同的可微分预测可直接用于频率论和贝叶斯推断，并通过探测器响应传递至重建可观测量以进行展开，同时还能提升计算效率。总之，ADoNIS提供了构建可微分中微子事例发生器的实用方案，适用于本文实现的特定物理模型之外的场景，本文还展示了其在与理论和实验相关的广泛任务中的应用。
英文摘要
Neutrino interaction generators are central to precision oscillation analyses, but conventional implementations do not directly provide derivatives of their predictions with respect to physics parameters, leaving analyses to obtain this dependence through reweighting, finite differences or external response models. We present ADoNIS, a fully differentiable neutrino interaction event generator that makes event reweighting and its gradients directly available, propagating exact derivatives through the nuclear ground state, hard-scattering amplitudes and stochastic intranuclear cascade. Following the physics choices of ACHILLES, we demonstrate agreement with its predictions across neutrino, electron and hadron probes, showing that differentiability is achieved without loss of physical fidelity. We then show how the resulting Jacobian exposes which measurements and regions of phase space constrain each parameter and how different probes break degeneracies, providing a quantitative tool for fit design, model tuning, measurement design and experiment design. The same differentiable predictions can be used directly in frequentist and Bayesian inference and carried through detector response to reconstructed observables for unfolding, while also yielding computational gains. In sum, ADoNIS provides a practical recipe for constructing differentiable neutrino event generators, applicable beyond the particular physics models implemented here, and this paper demonstrates their use across a wide range of tasks relevant to both theory and experiment.
