用AI提升粒子对撞机探测器分辨率，无需换硬件就能更准识别粒子。
Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction
- 用Transformer模型实现探测器数据的超分辨率重建
- 显著提升粒子重建精度，噪声抑制效果明显
- 适合未来高能对撞机软件升级，无需改动物理设备
为应对希格斯工厂与能量前沿设施的需求，未来对撞机正向高粒度量能器发展以提升重建质量。但此类探测器成本高、建造复杂，软件类超分辨率方法成为有吸引力的替代方案。本研究将超分辨率技术集成至类似LHC的重建流程中，有效提升量能器粒度并抑制噪声。结果表明，该软件预处理步骤可显著改善重建性能，无需改变探测器物理结构。为验证其效果，我们提出一种新型基于Transformer的粒子流模型，实现更高精度的粒子重建与更强可解释性。研究表明，超分辨率可直接应用于对撞机实验。
原文摘要 · Abstract (English)
In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the cost and construction complexity of such detectors is substantial, making software-based approaches like super-resolution an attractive alternative. This study explores integrating super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance calorimeter granularity and suppress noise. We find that this software preprocessing step significantly improves reconstruction quality without physical changes to the detector. To demonstrate its impact, we propose a novel transformer-based particle flow model that offers improved particle reconstruction quality and interpretability. Our results demonstrate that super-resolution can be readily applied at collider experiments.
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