论文标题
使用机器学习加快新的和升级的探测器研究：热量计案例
Using machine learning to speed up new and upgrade detector studies: a calorimeter case
论文作者
论文摘要
在本文中，我们讨论了高级机器学习技术使物理学家可以在设计阶段对探测器的现实操作模式进行深入研究。如果升级，则建议的方法可以应用于未来检测器和现有检测器的设计概念（CDR）和技术设计阶段（TDR）阶段。机器学习方法可能会加快对检测器配置的验证，并将自动化整个检测器R \＆D，通常伴随着大量分散的研究。我们介绍了将机器学习用于检测器R \＆D及其优化周期的方法，重点是LHCB检测器\ Cite {LHCLS3}的电磁量热升级的项目。证明了电磁训练仪的空间重建和到达特性的时间。
In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may speed up the verification of the possible detector configurations and will automate the entire detector R\&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R\&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The spatial reconstruction and time of arrival properties for the electromagnetic calorimeter were demonstrated.
