中微子望远镜中光子传播的可微神经代理模型
A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes
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中文总结 AI 辅助
研究针对中微子望远镜光子传播模拟计算成本高的问题，提出可微SIREN神经场模型candela，其模拟速度比现有方法快50-100倍，精度接近蒙特卡洛模拟，还可提供端到端梯度以优化散射介质特性。
中文摘要 AI 辅助
大体积中微子望远镜通过切伦科夫光推断中微子属性，但模拟数十亿光子在高散射冰或水中的传输计算成本极高。本文介绍candela，一种可微SIREN神经场，它学习立方公里探测器IceCube中微子天文台的光子格林函数，该探测器嵌入南极冰川冰中。给定点状能量沉积和传感器，它预测传感器处的预期光子产额和完整到达时间分布。通过将带电粒子能量沉积分解为点状源并叠加其预测的传感器响应，可模拟完整事例。candela在蒙特卡洛（MC）模拟上训练，生成事例的速度比现有方法快50至100倍，成本仅随中微子能量弱缩放。它在六个光子计数量级上，使中位产额保持在MC预期的2%以内，时间分布达到MC统计精度下限。该模型还提供关于事例参数的端到端梯度，为优化散射介质特性开辟了道路，而散射介质特性通常是中微子望远镜系统不确定性的主要来源。
英文摘要
Large-volume neutrino telescopes infer neutrino properties from Cherenkov light, but simulating the transport of billions of photons through highly scattering ice or water is computationally costly. We introduce candela, a differentiable SIREN neural field that learns the photon Green's function of the IceCube Neutrino Observatory, a cubic-kilometer detector embedded in Antarctic glacial ice. Given a point-like energy deposit and sensor, it predicts the expected photon yield and full arrival-time distribution at the sensor. Complete events are simulated by decomposing charged-particle energy deposits into point-like sources and superposing their predicted sensor responses. Trained on Monte-Carlo simulations, candela generates events $50$--$100\times$ faster than existing methods, with cost scaling only weakly with neutrino energy. It keeps median yields within $2\%$ of the MC expectation and timing distributions at the MC statistical floor across six photon-count decades. The model also provides end-to-end gradients with respect to event parameters and opens a path toward optimizing scattering-medium properties, which often dominate systematic uncertainties in neutrino telescopes.
发表机构
- Harvard University（哈佛大学）
- NSF IAIFI
- Massachusetts Institute of Technology（麻省理工学院）
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