论文标题
高能物理实验的事件生成器
Event Generators for High-Energy Physics Experiments
论文作者
论文摘要
我们概述了高能粒子物理学的蒙特卡洛事件发生器的状态。在实验需求和要求的指导下，我们重点介绍了积极发展的领域，以及未来改进的机会。在各种实验中采用的物理模型和算法特别强调。这些常见的主题在事件生成器开发中导致对最高能量和强度的物理学有更全面的了解，并允许对过去几十年来积累的大量数据进行测试。事件发生器开发的凝聚力方法将使这些模型得到进一步改进，并可以减少系统的不确定性，直接有助于未来的实验成功。事件生成器是计算工具的更大生态系统的一部分。它们通常涉及许多未知的模型参数，必须将其调整为实验数据，同时保持基础物理模型的完整性。制作这些数据，以及未来用户可以访问的分析是开放科学和数据保存的重要方面。它确保了各种实验的物理模型的一致性。
We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.
