CMS粒子流重构的机器学习
Machine Learning for Particle Flow Reconstruction at CMS
摘要：
我们提供了一个基于机器学习的粒子流算法CMS的实现细节。标准粒子流算法基于量热计簇和轨道重建稳定粒子，以提供利用多个检测器子系统的组合信息的全局事件重建，从而大幅改善射流和缺失横向能量等量。我们研究了粒子流向异构计算平台（如GPU）的可能演变，使用图形神经网络。机器学习的PF模型基于事件中的轨迹和热量计簇的完整列表来重建粒子候选者。为了验证，我们直接在CMS软件框架中确定所提出的算法与离线重建射流和丢失的横向能量接口时的物理性能。我们还报告了算法的计算性能，该算法在运行时和内存使用量与输入大小近似线性地缩放。
英文摘要：
We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy. We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network. The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. For validation, we determine the physics performance directly in the CMS software framework when the proposed algorithm is interfaced with the offline reconstruction of jets and missing transverse energy. We also report the computational performance of the algorithm, which scales approximately linearly in runtime and memory usage with the input size.
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