论文标题
与图神经网络的高占用成像量热量表中的端到端多粒子重建
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
论文作者
论文摘要
我们提出了一种端到端重建算法，以在下一代颗粒量热量器中从检测器命中构建粒子候选物，类似于CMS检测器的高光度升级的预见。该算法利用了距离加权的图神经网络，该网络通过对象冷凝（一种图形分割技术）训练。通过单次方法，重建任务与能量回归配对。我们从效率和能量解决方面描述了重建性能。此外，我们还显示了我们方法的JET重建性能，并讨论了其推理计算成本。据我们所知，这项工作是$ {\ cal o}（1000）$粒子在高亮度条件下具有200个堆积的$ {\ cal o}（1000）$粒子的第一个示例。
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of ${\cal O}(1000)$ particles in high-luminosity conditions with 200 pileup.
