论文标题
半参数$γ$ - 使用高斯流程和变异推断的建模
Semi-parametric $γ$-ray modeling with Gaussian processes and variational inference
论文作者
论文摘要
对银河系来源的不确定，不确定的弥漫性发射的不介绍会严重偏见天体物理伽玛射线数据的表征，尤其是在内部银河系的区域中，这种发射可以弥补在〜GEV能量时观察到的光子计数的80％以上。我们介绍了一类新的方法，这些方法使用高斯过程和变异推理来构建灵活的背景和信号模型进行伽马射线分析，目的是使对伽马射线天空的构成更加可靠地解释，尤其是专注于表征来自Fermi TeleScope数据的银河系中心潜在信号的潜在信号。
Mismodeling the uncertain, diffuse emission of Galactic origin can seriously bias the characterization of astrophysical gamma-ray data, particularly in the region of the Inner Milky Way where such emission can make up over 80% of the photon counts observed at ~GeV energies. We introduce a novel class of methods that use Gaussian processes and variational inference to build flexible background and signal models for gamma-ray analyses with the goal of enabling a more robust interpretation of the make-up of the gamma-ray sky, particularly focusing on characterizing potential signals of dark matter in the Galactic Center with data from the Fermi telescope.
