基于关联的内疚感：使用分层贝叶斯聚类发现Cosmic Ray来源
Guilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering
摘要：
地球不断地被带电的宇宙射线粒子所淹没，这些粒子是自然产生的原子核，以接近光速的速度运动。其中有能量超过5x10^19 eV的超高能宇宙射线粒子，比大型强子对撞机产生的最高能粒子的能量高出一千万倍。天体物理学的问题包括：什么现象加速粒子到如此高的能量，什么样的原子核被激活？此外，宇宙射线轨道的磁偏转使它们成为银河系和星系间磁场的潜在探测器。我们开发了一个贝叶斯分层模型，可以用来比较不同的关联模型之间的宇宙射线和源人口，使用贝叶斯因子。一个测量模型与方向的不确定性和帐户的非均匀天空曝光被incopored到模型中。该方法使我们能够了解天体物理参数，如控制源光度函数和宇宙磁场的参数。
英文摘要：
The Earth is continuously showered by charged cosmic ray particles, naturally produced atomic nuclei moving with velocity close to the speed of light. Among these are ultra high energy cosmic ray particles with energy exceeding 5x10^19 eV, which is ten million times more energetic than the most energetic particles produced at the Large Hadron Collider. Astrophysical questions include: what phenomenon accelerates particles to such high energies, and what sort of nuclei are energized? Also, the magnetic deflection of the trajectories of the cosmic rays makes them potential probes of galactic and intergalactic magnetic fields. We develop a Bayesian hierarchical model that can be used to compare different association models between the cosmic rays and source population, using Bayes factors. A measurement model with directional uncertainties and accounting for non-uniform sky exposure is incoporated into the model. The methodology allows us to learn about astrophysical parameters, such as those governing the source luminosity function and the cosmic magnetic field.
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