使用归一化流对量能器簇射进行归纳模拟
Inductive Simulation of Calorimeter Showers with Normalizing Flows
AI总结：
本文提出iCaloFlow框架，通过归纳式归一化流与师生蒸馏技术，解决高分辨率探测器模拟的内存瓶颈，在精细度高10至100倍的几何结构上实现快速高保真模拟。
摘要：
模拟粒子探测器响应是大型强子对撞机计算流程中开销最大的单一环节。近期研究表明，归一化流可以在达到前所未有的准确度的同时加速这一过程，但将该方法扩展到与未来探测器升级相关的更高分辨率时，会导致内存限制变得不可接受。为克服此问题，我们引入了Inductive CaloFlow（iCaloFlow），这是一个基于归纳式归一化流序列的快速探测器模拟框架，该序列在连续量能器层对的能量沉积模式上进行训练。我们进一步采用师生蒸馏技术，在不损失表达能力的情况下提高采样速度。正如我们使用CaloChallenge2022的Dataset 2和Dataset 3所展示的，iCaloFlow能够在比以往考虑的精细度高约10至100倍的探测器几何结构上，实现归一化流在执行快速、高保真模拟方面的潜力。
英文摘要：
Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy, but scaling this approach up to higher resolutions relevant for future detector upgrades leads to prohibitive memory constraints. To overcome this problem, we introduce Inductive CaloFlow (iCaloFlow), a framework for fast detector simulation based on an inductive series of normalizing flows trained on the pattern of energy depositions in pairs of consecutive calorimeter layers. We further use a teacher-student distillation to increase sampling speed without loss of expressivity. As we demonstrate with Datasets 2 and 3 of the CaloChallenge2022, iCaloFlow can realize the potential of normalizing flows in performing fast, high-fidelity simulation on detector geometries that are ~ 10 - 100 times higher granularity than previously considered.
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。
