电子-离子对撞机探测器的人工智能设计
Design of Detectors at the Electron Ion Collider with Artificial Intelligence
摘要：
设计人工智能（AI）是一个相对较新但活跃的跨学科研究领域。令人惊讶的是，当涉及到用AI设计探测器时，这是一个处于起步阶段的领域。电子离子对撞机是研究强相互作用的终极机器。EIC是一个大规模的实验，集成了一个探测器，延伸约$\pm$ 35米，包括中央，远前和远后区域。中央检测器的设计由多个子检测器进行，每个子检测器原则上以多维设计空间和也称为目标的多个设计标准为特征。Geant 4的模拟通常是计算密集型的，并且检测器设计的优化可能包括不可微项以及噪声目标。在这种情况下，人工智能可以提供最先进的解决方案，以有效的方式解决复杂的组合问题。特别是，其中一个原型合作项目，幼儿保育和教育，在探测器提案期间探索了使用多目标优化设计EIC探测器跟踪系统的可能性。本文概述了这些技术以及在EIC探测器提案期间取得的最新进展。未来的高能核物理实验可以利用基于人工智能的策略来设计更有效的探测器，通过优化物理标准驱动的性能并最大限度地降低实现成本。
英文摘要：
Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The Electron Ion Collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about $\pm$35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
