发表机构
Stony Brook University; Center for Frontiers in Nuclear Science, Stony Brook University（石溪大学; 石溪大学核科学前沿中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对相对论重离子碰撞中量能器数据缺失问题，以预训练量能器扩散模型为先验，系统比较多种扩散修复算法并建立通用验证策略，实现缺失探测器信息的概率重建。
AI 中文摘要
局部探测器效率低下会产生不完整的量能器数据，限制了精确测量的能力。我们从贝叶斯视角解决相对论重离子碰撞中的这一问题，使用预训练的量能器扩散模型作为先验，在周围测量的条件下重建缺失信号。本工作系统比较了多种基于扩散的修复算法，通过能量响应、空间偏差和不确定性校准的贝叶斯后验诊断评估其性能，还分析了不同碰撞中心度和掩蔽区域大小下的重建保真度，为缺失探测器信息的概率重建建立了通用验证策略。
英文摘要
Localized detector inefficiencies produce incomplete calorimeter data that limit the ability to perform precision measurements. We address this problem in relativistic heavy-ion collisions from a Bayesian perspective using pretrained calorimeter diffusion models as priors to reconstruct the missing signal conditioned on surrounding measurements. In this work, we conduct a systematic comparison of several diffusion-based inpainting algorithms, whose performance is evaluated using Bayesian posterior diagnostics of energy response, spatial bias, and uncertainty calibration. The reconstruction fidelity is also analyzed across collision centralities and masked region sizes. This study establishes a general validation strategy for probabilistic reconstruction of missing detector information.
Comments21 pages, 15 figures
