论文标题
量热计研发的ML辅助方法
ML-assisted versatile approach to Calorimeter R&D
论文作者
论文摘要
用于HEP的新实验和正在进行的实验的高级检测器R＆D需要作为检测器设计优化过程的一部分进行计算密集和详细的模拟。我们提出了一种基于机器学习的多功能方法，该方法可以替代流程中最密集的步骤，同时将GEANT4精度保留到详细信息中。该方法涵盖了从事件产生到物理性能评估的整个检测器表示。该方法允许使用任意模块布置，不同的信号和背景条件，可调的重建算法以及所需的物理性能指标。虽然结合了从梁测试获得的检测器和电子原型的特性，但该方法变得更加通用。我们关注LHCB量热仪的II期升级，这是在高光度下运行的要求。我们讨论了该方法和特定估计的一般设计，包括在不同的堆积条件下未来LHCB量热计的空间和能量分辨率。
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy to details. The approach covers entire detector representation from the event generation to the evaluation of the physics performance. The approach allows the use of arbitrary modules arrangement, different signal and background conditions, tunable reconstruction algorithms, and desired physics performance metrics. While combined with properties of detector and electronics prototypes obtained from beam tests, the approach becomes even more versatile. We focus on the Phase II Upgrade of the LHCb Calorimeter under the requirements on operation at high luminosity. We discuss the general design of the approach and particular estimations, including spatial and energy resolution for the future LHCb Calorimeter setup at different pile-up conditions.
