论文标题

朝着可靠的探测器的神经产生建模

Towards Reliable Neural Generative Modeling of Detectors

论文作者

Anderlini, Lucio, Barbetti, Matteo, Derkach, Denis, Kazeev, Nikita, Maevskiy, Artem, Mokhnenko, Sergei

论文摘要

大型强子对撞机和下一代对撞机实验的未来数据的亮度不断提高,需要产生空前的模拟事件。如此大规模的生产要求大量有价值的计算资源。这使得需要使用新方法来生成事件和探测器响应的模拟。在本文中,我们讨论了生成对抗网络(GAN)在LHCB实验事件的模拟中的应用。我们强调在甘施应用中的主要陷阱,并详细研究系统效应。提出的结果基于LHCB Cherenkov检测器的GEANT4模拟。

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.

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