树的分支：在高能物理中处理具有离散和分支随机性的程序求导
Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics
AI总结：
本文提出用多种梯度估计技术使高能物理中带离散和分支随机性的程序可微分，开发了首个完全可微的分支程序，用于探测器设计等优化。
摘要：
我们提出应用多种梯度估计技术，以实现高能物理中具有离散随机性的程序的可微分化。此类程序在高能物理中很常见，因为存在分支过程和基于聚类的分析。因此，对这类程序求导可以为基于梯度的优化开辟道路，应用于探测器设计优化、模拟器调参或数据分析与重建优化。我们讨论了多种可能的梯度估计策略，包括最新的Stochastic AD方法，并在简化的探测器设计实验中进行了比较。通过这项工作，据我们所知，我们开发了第一个完全可微的分支程序。
英文摘要：
We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
