标题： ExpertSim：使用生成专家混合的快速粒子探测器模拟 显示英文标题

标题： ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts

摘要： 模拟探测器响应是理解欧洲核子研究中心大型强子对撞机中粒子碰撞内部机制的关键部分。 此类模拟目前使用统计蒙特卡罗方法进行，这些方法计算成本高昂，并对欧洲核子研究中心的计算网格造成显著压力。 因此，最近的建议提倡使用生成式机器学习方法，以实现更高效的模拟。 然而，数据分布在不同模拟中变化很大，这很难用现成的方法捕捉。 在本研究中，我们提出了 ExpertSim - 一种针对ALICE实验中的零度量能器量身定制的深度学习模拟方法。 我们的方法利用了 生成专家混合架构，其中每个专家专门负责模拟数据的不同子集。 这使得生成过程更加精确和高效，因为每个专家专注于量能器响应的一个特定方面。 ExpertSim不仅提高了准确性，而且相比传统蒙特卡罗方法提供了显著的速度提升，为欧洲核子研究中心粒子物理实验中的高效探测器模拟提供了一个有前景的解决方案。 我们在https://github.com/patrick-bedkowski/expertsim-mix-of-generative-experts提供代码。 显示英文摘要

摘要： Simulating detector responses is a crucial part of understanding the inner workings of particle collisions in the Large Hadron Collider at CERN. Such simulations are currently performed with statistical Monte Carlo methods, which are computationally expensive and put a significant strain on CERN's computational grid. Therefore, recent proposals advocate for generative machine learning methods to enable more efficient simulations. However, the distribution of the data varies significantly across the simulations, which is hard to capture with out-of-the-box methods. In this study, we present ExpertSim - a deep learning simulation approach tailored for the Zero Degree Calorimeter in the ALICE experiment. Our method utilizes a Mixture-of-Generative-Experts architecture, where each expert specializes in simulating a different subset of the data. This allows for a more precise and efficient generation process, as each expert focuses on a specific aspect of the calorimeter response. ExpertSim not only improves accuracy, but also provides a significant speedup compared to the traditional Monte-Carlo methods, offering a promising solution for high-efficiency detector simulations in particle physics experiments at CERN. We make the code available at https://github.com/patrick-bedkowski/expertsim-mix-of-generative-experts.