基于Swin Transformer与归一化流模型的成像切伦科夫探测器深度（更深层）重建
Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models
AI总结：
本研究提出Deep(er)RICH方法，结合层级Swin Transformer与归一化流，改进了DIRC等切伦科夫探测器的粒子鉴别速度与精度，首次实现快速准确模拟，解决光子传输模拟瓶颈，可支撑EIC等前沿实验。
摘要：
成像切伦科夫探测器是核物理与粒子物理实验中粒子鉴别（PID）的关键设备。快速重建算法对于近实时对准、校准、数据质量控制以及高效分析至关重要。在未来的电子-离子对撞机（EIC）中，ePIC探测器将在强子方向配备双环形成像切伦科夫（dual-RICH）探测器，在桶部配备内反射切伦科夫探测器（DIRC），在电子方向配备近焦RICH探测器。本文聚焦于DIRC探测器，该探测器具有复杂的击中模式，同时也用于杰斐逊实验室（JLab）GlueX实验中介子和K介子的粒子鉴别。我们提出了Deep(er)RICH，这是开创性工作DeepRICH的扩展方案，相比传统方法实现了更优、更快的粒子鉴别效果，并且首次实现了快速且准确的模拟。这一进展解决了切伦科夫探测器模拟中涉及光子穿过复杂光学元件传输的主要瓶颈。我们的研究结果得益于视觉Transformer领域的进展，具体为层级Swin Transformer与归一化流。这些方法能够直接从真实数据中学习，并重建复杂的拓扑结构。最后我们讨论了本工作的意义与未来扩展方向，该工作可为未来EIC等多个前沿实验提供粒子鉴别能力。
英文摘要：
Imaging Cherenkov detectors are crucial for particle identification (PID) in nuclear and particle physics experiments. Fast reconstruction algorithms are essential for near real-time alignment, calibration, data quality control, and efficient analysis. At the future Electron-Ion Collider (EIC), the ePIC detector will feature a dual Ring Imaging Cherenkov (dual-RICH) detector in the hadron direction, a Detector of Internally Reflected Cherenkov (DIRC) in the barrel, and a proximity focus RICH in the electron direction. This paper focuses on the DIRC detector, which presents complex hit patterns and is also used for PID of pions and kaons in the GlueX experiment at JLab. We present Deep(er)RICH, an extension of the seminal DeepRICH work, offering improved and faster PID compared to traditional methods and, for the first time, fast and accurate simulation. This advancement addresses a major bottleneck in Cherenkov detector simulations involving photon tracking through complex optical elements. Our results leverage advancements in Vision Transformers, specifically hierarchical Swin Transformer and normalizing flows. These methods enable direct learning from real data and the reconstruction of complex topologies. We conclude by discussing the implications and future extensions of this work, which can offer capabilities for PID for multiple cutting-edge experiments like the future EIC.
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。
