论文标题
通过快速模拟的未来山脉的喷气风味标签
Jet Flavour Tagging for Future Colliders with Fast Simulation
论文作者
论文摘要
喷射风味识别算法对于最大程度地提高了未来对撞机实验的物理潜力至关重要。这项工作描述了一组新型的工具，允许对粒子水平可观察物进行逼真的模拟和重建，这些粒子水平可观察到是喷气风味识别所需的成分。已经开发了用于重建带电粒子的轨道参数和协方差矩阵的算法，以进行任意跟踪子检测器几何。已经实施了允许使用飞行时间和电离能量损失信息进行粒子识别的其他模块。已经开发了基于图神经网络架构并利用所有可用粒子级信息的喷气风味识别算法。使用FCC-EE IDEA探测器原型评估了不同检测器设计假设对风味标记性能的影响。
Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level observables that are necessary ingredients to jet flavour identification. An algorithm for reconstructing the track parameters and covariance matrix of charged particles for an arbitrary tracking sub-detector geometries has been developed. Additional modules allowing for particle identification using time-of-flight and ionizing energy loss information have been implemented. A jet flavour identification algorithm based on a graph neural network architecture and exploiting all available particle level information has been developed. The impact of different detector design assumptions on the flavour tagging performance is assessed using the FCC-ee IDEA detector prototype.
