用可微分流提升高能物理模拟效率，减少预训练数据依赖。
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
- 基于可微分流的FAB方法，边训练边评估目标密度。
- 高维场景下采样效率更高，所需目标评估次数更少。
- 适合需要高效模拟的高能物理研究者使用。
高能物理需要从复杂但解析可处理的概率分布（称为矩阵元）中生成大量模拟数据。由于计算效率高，归一化流等代理模型正逐渐成为该任务的热门选择。本文采用基于流退火重要性采样引导（FAB）的方法，在训练过程中评估可微分的目标密度，从而避免预先生成大量训练数据。结果表明，相较于其他方法，FAB在高维情况下具有更高的采样效率，且所需目标函数评估次数更少。
原文摘要 · Abstract (English)
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.
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