用物理启发的生成模型，让粒子探测器模拟快421倍。
Inverse Autoregressive Flows for Zero Degree Calorimeter fast simulation
- 基于归一化流构建师生框架，融合物理知识提升模拟精度。
- 在零度量能器上实现421倍速度提升，且抗异常数据干扰。
- 适合高能物理仿真加速，尤其需快速生成大量事件的研究者。
物理驱动的机器学习将领域知识融入学习过程，兼顾准确性与鲁棒性。本文针对欧洲核子研究中心ALICE实验中的零度量能器（ZDC）模拟问题，提出一种新损失函数与输出变异性缩放机制，增强对粒子簇在探测器中空间分布和形态的建模能力，并减轻罕见伪影对训练的影响。采用归一化流（NFs）的师生生成框架，实验表明该方法不仅优于传统数据驱动模型，且相比现有文献中的NF实现，模拟速度提升421倍。
原文摘要 · Abstract (English)
Physics-based machine learning blends traditional science with modern data-driven techniques. Rather than relying exclusively on empirical data or predefined equations, this methodology embeds domain knowledge directly into the learning process, resulting in models that are both more accurate and robust. We leverage this paradigm to accelerate simulations of the Zero Degree Calorimeter (ZDC) of the ALICE experiment at CERN. Our method introduces a novel loss function and an output variability-based scaling mechanism, which enhance the model's capability to accurately represent the spatial distribution and morphology of particle showers in detector outputs while mitigating the influence of rare artefacts on the training. Leveraging Normalizing Flows (NFs) in a teacher-student generative framework, we demonstrate that our approach not only outperforms classic data-driven model assimilation but also yields models that are 421 times faster than existing NF implementations in ZDC simulation literature.
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