Yale University, New Haven, Connecticut 06520, USA.
Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Phys Rev Lett. 2018 Jan 26;120(4):042003. doi: 10.1103/PhysRevLett.120.042003.
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theoretical modeling assumptions. Petabytes of simulated data are needed to develop analysis techniques, though they are expensive to generate using existing algorithms and computing resources. The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter are the most computationally demanding steps in the simulation pipeline. We therefore introduce a deep neural network-based generative model to enable high-fidelity, fast, electromagnetic calorimeter simulation. There are still challenges for achieving precision across the entire phase space, but our current solution can reproduce a variety of particle shower properties while achieving speedup factors of up to 100 000×. This opens the door to a new era of fast simulation that could save significant computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond.
大型强子对撞机（LHC）的物理学家依赖于对粒子碰撞的详细模拟，以根据不同的理论建模假设来构建对实验数据外观的预期。尽管使用现有算法和计算资源生成它们很昂贵，但需要 PB 级的模拟数据来开发分析技术。在模拟管道中，对探测器的建模以及对粒子级联与量热器中的材料相互作用的精确描述是计算要求最高的步骤。因此，我们引入了一种基于深度神经网络的生成模型，以实现高保真、快速的电磁量热器模拟。在整个相空间中实现精度仍然存在挑战，但我们当前的解决方案可以复制各种粒子簇射特性，同时实现高达 100000 倍的加速。这为快速模拟的新时代打开了大门，这种模拟可以节省大量的计算时间和磁盘空间，同时扩展 LHC 及其他地方的物理搜索和精密测量的范围。
