用专家混合模型统一处理探测器的模拟、识别与降噪，速度快效果好。
Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector
- 共享一个Transformer主干，跨任务通用，避免多管道碎片化。
- 在全动量范围内性能优于或媲美传统方法，支持粒子条件生成。
- 适合需要高效、统一处理方案的高能物理实验分析人员。
本文将基于专家混合（Mixture of Experts）的基座模型应用于杰斐逊实验室的GlueX DIRC探测器，展示其作为快速模拟、粒子识别及光子击中噪声过滤的统一框架的实用性。该方法通过单一共享的Transformer主干实现多任务处理，消除了任务专用流程的碎片化，同时在多个方面保持甚至超越现有方法的性能。模型直接处理低层探测器输入，采用分空间与时间词汇的逐击自回归生成，并结合连续运动学条件；其专家混合架构支持π介子与K介子的类别条件生成。在全动量相空间下，与标准几何重建及先前深度学习方法对比，验证了该基座模型无需架构修改即可有效迁移至该探测器。本工作为GlueX DIRC分析提供了可扩展且实用的替代方案。
原文摘要 · Abstract (English)
We present a Mixture-of-Experts-based foundation model applied to the GlueX DIRC detector at Jefferson Lab, demonstrating its utility as a unified framework for fast simulation, particle identification, and hit-level noise filtering of Cherenkov photons. By leveraging a single shared transformer backbone across all tasks, the approach eliminates the fragmentation of task-specific pipelines while maintaining competitive-and in several cases superior-performance relative to established methods. The model operates directly on low-level detector inputs, performing hit-by-hit autoregressive generation over split spatial and temporal vocabularies with continuous kinematic conditioning, and supports class-conditional generation of pions and kaons through its Mixture-of-Experts architecture. We benchmark against the standard geometrical reconstruction and prior deep learning methods across the full kinematic phase space of the GlueX DIRC, demonstrating that the foundation model framework transfers effectively to this detector without architectural modification. This work positions the foundation model as a practical and scalable alternative to the suite of task-specific models currently proposed for GlueX DIRC analysis.
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