论文标题
检测是截断：研究边缘神经比估计的源群体
Detection is truncation: studying source populations with truncated marginal neural ratio estimation
论文作者
论文摘要
天体物理来源人口参数的统计推断具有挑战性。它需要考虑选择效果的考虑，这源于分析管道本身引入的明亮检测到的未检测到的昏暗来源之间的人为分离。我们表明，这些效果可以在基于顺序仿真的推理的背景下自言自语。我们的方法将源检测和基于目录的推断构成的原则框架，该框架源自截短的边际神经比率估计（TMNRE）算法。它依赖于意识到可以将检测解释为先验截断。我们概述了该算法，并显示了第一个有希望的结果。
Statistical inference of population parameters of astrophysical sources is challenging. It requires accounting for selection effects, which stem from the artificial separation between bright detected and dim undetected sources that is introduced by the analysis pipeline itself. We show that these effects can be modeled self-consistently in the context of sequential simulation-based inference. Our approach couples source detection and catalog-based inference in a principled framework that derives from the truncated marginal neural ratio estimation (TMNRE) algorithm. It relies on the realization that detection can be interpreted as prior truncation. We outline the algorithm, and show first promising results.
