k4Clue 软件包：借助 CLUE 算法赋能未来对撞机实验
The k4Clue package: Empowering Future Collider Experiments with the CLUE Algorithm
AI总结：
本研究开发了集成于Gaudi框架、支持EDM4hep格式的k4Clue软件包，验证CLUE算法在多种未来对撞机探测器上的优异重建与时序性能，证明其跨实验的通用性与应用潜力。
摘要：
高粒度量能器在现代粒子物理实验中已变得愈发关键，其重要性在未来还将进一步提升。能量聚类（CLUstering of Energy, CLUE）算法在为 CMS 实验二期升级研发的高粒度量能器（High Granularity Calorimeter, HGCAL）的量能器击中聚类任务中表现出优异性能。本文研究了 CLUE 算法对未来对撞机实验的适用性，并测试了其在 HGCAL 软件重建框架之外的性能。为此，我们开发了全新的 k4Clue 软件包，该包目前已完全集成至 Gaudi 软件框架中，且支持输入输出采用 EDM4hep 数据格式。我们在三种面向未来对撞机的探测器上验证了 CLUE 的性能：分别是用于 CLIC 加速器的 CLICdet、用于 FCC-ee 对撞机的 CLD，以及同样为 FCC-ee 提出的第二种基于惰性液体技术的量能器。研究发现，即使存在噪声，CLUE 对单伽马事件也能实现出色的重建性能，且表现优于其他基线算法。此外，CLUE 展现出优异的时序性能，其表现超过其他算法，且性能与输入击中的数量无关。这项工作凸显了 CLUE 算法在各类实验和探测器中的适配性与通用性，也体现了该算法在 CMS 之外的未来高能物理实验中的应用潜力。
英文摘要：
High granularity calorimeters have become increasingly crucial in modern particle physics experiments, and their importance is set to grow even further in the future. The CLUstering of Energy (CLUE) algorithm has shown excellent performance in clustering calorimeter hits in the High Granularity Calorimeter (HGCAL) developed for the Phase-2 upgrade of the CMS experiment. In this paper, we investigate the suitability of the CLUE algorithm for future collider experiments and test its capabilities outside the HGCAL software reconstruction. To this end, we developed a new package, k4Clue, which is now fully integrated into the Gaudi software framework and supports the EDM4hep data format for inputs and outputs. We demonstrate the performance of CLUE in three detectors for future colliders: CLICdet for the CLIC accelerator, CLD for the FCC-ee collider and a second calorimeter based on Noble Liquid technology also proposed for FCC-ee. We find excellent reconstruction performance for single gamma events, even in the presence of noise, and also compared with other baseline algorithms. Moreover, CLUE demonstrates impressive timing capabilities, outperforming the other algorithms and independently of the number of input hits. This work highlights the adaptability and versatility of the CLUE algorithm for a wide range of experiments and detectors and the algorithm's potential for future high-energy physics experiments beyond CMS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
