The CMS detector will be upgraded to maintain, or even improve, the physics acceptance under the harsh data taking conditions foreseen during the High-Luminosity LHC operations. In particular, the trigger system (Level-1 and High Level Triggers) will be completely redesigned to utilize detailed information from sub-detectors at the bunch crossing rate: the upgraded Global Trigger will use high-precision trigger objects to provide the Level-1 decision. Besides cut-based algorithms, novel machine-learning-based algorithms will also be included in the Global Trigger to achieve a higher selection efficiency and detect unexpected signals. Implementation of these novel algorithms is presented, focusing on how the neural network models can be optimized to ensure a feasible hardware implementation. The performance and resource usage of the optimized neural network models are discussed in detail.

中文翻译：

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HL-LHC CMS 1 级全局触发升级的基于神经网络的条件的设计和实现

CMS 探测器将进行升级，以维持甚至提高高亮度大型强子对撞机操作期间预见的恶劣数据采集条件下的物理可接受性。特别是，触发系统（Level-1和High Level Triggers）将被完全重新设计，以利用子探测器在束交叉率下的详细信息：升级后的Global Trigger将使用高精度触发对象来提供Level-1决定。除了基于剪切的算法外，全局触发中还将包含新颖的基于机器学习的算法，以实现更高的选择效率并检测意外信号。介绍了这些新颖算法的实现，重点关注如何优化神经网络模型以确保可行的硬件实现。详细讨论了优化神经网络模型的性能和资源使用。