论文标题
在重建PADME电磁热量计的信号中使用人工智能
Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter
论文作者
论文摘要
PADME设备是在INFN的Frascati国家实验室建造的，以寻找通过该过程$ e^+ e^ - \ RightarrowA'γ$生产的深色光子（$ a'U $）。 PADME探测器的中心分量是电磁热量表，由616个BGO晶体组成，用于测量最终状态光子的能量和位置。短束持续时间内的远光粒子多样性需要可靠的识别和重叠信号的测量。已经开发了一种基于回归机器学习的算法，以消除高效率的近距离事件，并精确地重建命中幅度和以次纳秒分辨率的时间。介绍和讨论了算法的性能和导致实现结果的改进顺序。
The PADME apparatus was built at the Frascati National Laboratory of INFN to search for a dark photon ($A'$) produced via the process $e^+ e^- \rightarrow A' γ$. The central component of the PADME detector is an electromagnetic calorimeter composed of 616 BGO crystals dedicated to the measurement of the energy and position of the final state photons. The high beam particle multiplicity over a short bunch duration requires reliable identification and measurement of overlapping signals. A regression machine-learning-based algorithm has been developed to disentangle with high efficiency close-in-time events and precisely reconstruct the amplitude of the hits and the time with sub-nanosecond resolution. The performance of the algorithm and the sequence of improvements leading to the achieved results are presented and discussed.
