发表机构
Institute of Astronomy, University of Cambridge; Instituto de Estructura de la Materia (IEM), CSIC; Department of Physics and Astronomy, University of Texas Rio Grande Valley（剑桥大学天文学研究所; 西班牙国家研究委员会物质结构研究所; 德克萨斯大学里奥格兰德河谷分校物理与天文系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究采用脉冲星计时阵列（PTA）玩具模型，解释基于模拟的推理（SBI）方法如何及在何种情况下可改进引力波背景各向异性的经典探测统计量，助力理解神经网络从PTA数据中学习的机制。
AI 中文摘要
近年来，基于模拟的推理（SBI）方法被提出以应对现有及规划中的引力波实验面临的若干数据分析挑战。例如，SBI分类近期被证实可显著提升脉冲星计时阵列（PTA）数据中各向异性探测的前景。本研究采用简单的PTA玩具模型，以更具教学性的方式解释基于SBI的探测如何、在何种情况下能改进引力波背景各向异性的经典探测统计量。
英文摘要
In recent years, simulation-based inference (SBI) methods have been proposed to address several data-analysis challenges faced by existing and planned gravitational-wave experiments. For example, SBI classification has recently been shown to significantly improve the prospects for detecting anisotropies in pulsar timing array (PTA) data. In this work, we use a simple toy model of a PTA to provide a more pedagogical explanation of how, and under which circumstances, SBI-based detection can improve on classical detection statistics for gravitational-wave background anisotropies.
Comments12 pages, 4 figures
