标题： 一种用于超高能宇宙射线源关联和参数推断的贝叶斯框架 显示英文标题

标题： A Bayesian Framework for UHECR Source Association and Parameter Inference

摘要： 潜在超高能宇宙射线（UHECRs）来源的识别仍然具有挑战性，这是由于磁场偏转和传播损失，尤其是对原子核来说更为显著。在本工作的先前版本中，我们提出了一种基于贝叶斯推断的UHECR天文学方法，通过显式建模传播和磁场偏转效应。逐事件的质量信息有望对这些参数提供更严格的约束，并有助于识别未知来源。然而，通过皮埃尔·奥热天文台地表探测器的观测测量得到的平均质量已经表明，UHECR的质量可以通过其统计平均值很好地表示。在本次贡献中，我们提出了一个框架，利用$\ln A$的能量和质量矩来推断UHECR的源参数，包括源处的质量组成。我们使用基于皮埃尔·奥热天文台和望远镜阵列项目的模拟数据集来展示我们模型的性能。我们的模型可以轻松应用于当前可用的数据，并讨论了我们的结果对UHECR源识别的意义。 显示英文摘要

摘要： The identification of potential sources of ultra-high-energy cosmic rays (UHECRs) remains challenging due to magnetic deflections and propagation losses, which are particularly strong for nuclei. In previous iterations of this work, we proposed an approach for UHECR astronomy based on Bayesian inference through explicit modelling of propagation and magnetic deflection effects. The event-by-event mass information is expected to provide tighter constraints on these parameters and to help identify unknown sources. However, the measurements of the average mass through observations from the surface detectors at the Pierre Auger Observatory already indicate that the UHECR masses are well represented through its statistical average. In this contribution, we present our framework which uses energy and mass moments of $\ln A$ to infer the source parameters of UHECRs, including the mass composition at the source. We demonstrate the performance of our model using simulated datasets based on the Pierre Auger Observatory and Telescope Array Project. Our model can be readily applied to currently available data, and we discuss the implications of our results for UHECR source identification.