Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics, where the spatial resolution of calorimeters has a crucial impact. This study explores the integration of super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance the granularity of calorimeter data and suppress noise. We find that this software preprocessing step can significantly improve reconstruction quality without physical changes to detectors. To demonstrate the impact of our approach, we propose a novel particle flow model that offers enhanced particle reconstruction quality and interpretability. These advancements underline the potential of super-resolution to impact both current and future particle physics experiments.

翻译：精确地从探测器数据中重建粒子是实验粒子物理学中的一个关键挑战，其中量能器的空间分辨率具有至关重要的影响。本研究探索将超分辨率技术集成到类似LHC的重建流程中，以有效增强量能器数据的粒度并抑制噪声。我们发现，这一软件预处理步骤能够在不改变探测器物理结构的情况下，显著提升重建质量。为展示我们方法的影响，我们提出了一种新颖的粒子流模型，该模型提供了增强的粒子重建质量和可解释性。这些进展凸显了超分辨率技术对当前及未来粒子物理实验产生影响的潜力。