In High Energy Physics simulations play a crucial role in unraveling the
complexities of particle collision experiments within CERN's Large Hadron
Collider. Machine learning simulation methods have garnered attention as
promising alternatives to traditional approaches. While existing methods mainly
employ Variational Autoencoders (VAEs) or Generative Adversarial Networks
(GANs), recent advancements highlight the efficacy of diffusion models as
state-of-the-art generative machine learning methods. We present the first
simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on
diffusion models, achieving the highest fidelity compared to existing
baselines. We perform an analysis of trade-offs between generation times and
the simulation quality. The results indicate a significant potential of latent
diffusion model due to its rapid generation time.

利用扩散模型，我们首次使用模拟方法对 ALICE 实验中的 Zero Degree Calorimeter（ZDC）进行了模拟，与现有基线相比实现了最高保真度，结果表明潜在扩散模型具有快速生成时间的显著潜力。

用于快速模拟 CERN 粒子碰撞的生成扩散模型

Generative Diffusion Models for Fast Simulations of Particle Collisions  at CERN

Simulating detector responses is a crucial part of understanding the
inner-workings of particle collisions in the Large Hadron Collider at CERN. The
current reliance on statistical Monte-Carlo simulations strains CERN's
computational grid, underscoring the urgency for more efficient alternatives.
Addressing these challenges, recent proposals advocate for generative machine
learning methods. In this study, we present an innovative deep learning
simulation approach tailored for the proton Zero Degree Calorimeter in the
ALICE experiment. Leveraging a Generative Adversarial Network model with
Selective Diversity Increase loss, we directly simulate calorimeter responses.
To enhance its capabilities in modeling a broad range of calorimeter response
intensities, we expand the SDI-GAN architecture with additional regularization.
Moreover, to improve the spatial fidelity of the generated data, we introduce
an auxiliary regressor network. Our method offers a significant speedup when
comparing to the traditional Monte-Carlo based approaches.

利用生成对抗网络模型以及选择性多样性增强损失来模拟大型强子对撞机中 ALICE 实验的 Zero Degree Calorimeter 产生的探测器响应，从而提供了与传统蒙特卡洛方法相比显著加速的方法。