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.
翻译:我们提出了一个端到端的重建算法,用于从下一代颗粒热量的探测点点中建立粒子候选物,类似于为CMS探测器的高光度升级预设的粒子粒子。算法利用了远程加权的图形神经网络,经过了物体凝结培训,一种图形分解技术。通过单发方法,重建任务与能源回归相配合。我们用效率以及能源分辨率来描述重建绩效。此外,我们展示了我们方法的喷气式重建绩效,并讨论了其推论计算成本。据我们所知,这项工作是用200堆积的重塑高光度条件下的1 000美元微粒的单发热度重建的第一个实例。