The pursuit of understanding fundamental particle interactions has reached
unparalleled precision levels. Particle physics detectors play a crucial role
in generating low-level object signatures that encode collision physics.
However, simulating these particle collisions is a demanding task in terms of
memory and computation which will be exasperated with larger data volumes, more
complex detectors, and a higher pileup environment in the High-Luminosity LHC.
The introduction of "Fast Simulation" has been pivotal in overcoming
computational bottlenecks. The use of deep-generative models has sparked a
surge of interest in surrogate modeling for detector simulations, generating
particle showers that closely resemble the observed data. Nonetheless, there is
a pressing need for a comprehensive evaluation of their performance using a
standardized set of metrics. In this study, we conducted a rigorous evaluation
of three generative models using standard datasets and a diverse set of metrics
derived from physics, computer vision, and statistics. Furthermore, we explored
the impact of using full versus mixed precision modes during inference. Our
evaluation revealed that the CaloDiffusion and CaloScore generative models
demonstrate the most accurate simulation of particle showers, yet there remains
substantial room for improvement. Our findings identified areas where the
evaluated models fell short in accurately replicating Geant4 data.

利用物理、计算机视觉和统计学的多元指标对三种生成模型进行了全面评估，发现 CaloDiffusion 和 CaloScore 生成模型具有最准确的粒子阵列模拟，但仍有改进的空间，发现了评估模型在准确复制 Geant4 数据方面的局限性。

能量沉积模拟中生成模型的全面评估

A Comprehensive Evaluation of Generative Models in Calorimeter Shower  Simulation

In High Energy Physics, detailed and time-consuming simulations are used for
particle interactions with detectors. To bypass these simulations with a
generative model, the generation of large point clouds in a short time is
required, while the complex dependencies between the particles must be
correctly modelled. Particle showers are inherently tree-based processes, as
each particle is produced by the decay or detector interaction of a particle of
the previous generation. In this work, we present a significant extension to
DeepTreeGAN, featuring a critic, that is able to aggregate such point clouds
iteratively in a tree-based manner. We show that this model can reproduce
complex distributions, and we evaluate its performance on the public JetNet 150
dataset.

本文介绍了一种扩展了 DeepTreeGAN 的模型，其中包含一个评论者，它能够以基于树的方式迭代地聚合点云，模拟高能物理中的粒子模拟与探测器相互作用，以实现快速生成大规模点云，并准确建模粒子之间的复杂依赖关系。我们展示了该模型能够重现复杂分布，并对公开的 JetNet 150 数据集进行了评估。

深度树生成对抗网络 v2：点云的迭代池化

DeepTreeGANv2: Iterative Pooling of Point Clouds

Simulations of particle showers in calorimeters are computationally
time-consuming, as they have to reproduce both energy depositions and their
considerable fluctuations. A new approach to ultra-fast simulations are
generative models where all calorimeter energy depositions are generated
simultaneously. We use GEANT4 simulations of an electron beam impinging on a
multi-layer electromagnetic calorimeter for adversarial training of a generator
network and a critic network guided by the Wasserstein distance. The generator
is constraint during the training such that the generated showers show the
expected dependency on the initial energy and the impact position. It produces
realistic calorimeter energy depositions, fluctuations and correlations which
we demonstrate in distributions of typical calorimeter observables. In most
aspects, we observe that generated calorimeter showers reach the level of
showers as simulated with the GEANT4 program.

本文提出了基于生成式模型的高效粒子淋浴模拟方法，并使用对抗训练的方式训练生成器和评论者网络以生成与 GEANT4 程序模拟类似的粒子淋浴，从而在大多数方面达到了实现高质量粒子淋浴模拟的预期目标。