论文标题
在Atlas中快速光子淋浴模拟的深生成模型
Deep generative models for fast photon shower simulation in ATLAS
论文作者
论文摘要
大规模生产高度准确的模拟事件样本的需求，用于大型强子撞机的ATLAS实验广泛的物理计划，激发了新的模拟技术的开发。研究了深度学习算法，变异自动编码器和生成对抗网络的最新成功，以建模地图集电磁热量表对各种能量光子的中央区域的响应。将合成淋浴的特性与使用Geant4的完整检测器仿真的淋浴进行了比较。尽管某些淋浴形状分布的建模需要更多的细化，但变异自动编码器和生成对抗网络都能够快速模拟具有正确的总能量和随机性的电磁淋浴。这项可行性研究表明，将来使用此类算法进行Atlas快速热量计模拟的潜力，并显示了一种补充当前模拟技术的可能方法。
The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using GEANT4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.
