论文标题
替代模型的新方向和高能量物理探测器模拟的可区分编程
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
论文作者
论文摘要
未来实验设施中高能量物理检测器仿真的计算成本将超过当前可用资源。为了克服这一挑战，正在探索使用机器学习方法的替代模型的新想法，以替代计算昂贵的组件。此外，已经提出了可区分的编程作为互补方法，提供了可控且可扩展的仿真例程。在本文档中，在2021年粒子物理社区计划练习（“雪地”）的背景下，讨论了针对检测器模拟的替代模型的新努力以及应用于检测器仿真的差异编程。
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').
