标题： 高纬度费米伽马射线空间望远镜（Fermi-LAT）天空源计数分布的鲁棒神经确定 显示英文标题

标题： A robust neural determination of the source-count distribution of the Fermi-LAT sky at high latitudes

摘要： 在过去16年里，费米大面积望远镜（LAT）显著提升了我们对GeV伽马射线天空的认识，但仍有许多关键问题未解决——比如各向同性伽马射线背景的组成、费米气泡的起源，以及潜在存在的来自奇异物理（如暗物质）的信号。 解决这些挑战需要复杂的天体物理建模和强大的统计方法，能够处理高维参数空间。 在这项工作中，我们使用基于模拟的推理（SBI）通过神经比估计分析了14年的高纬度（$|b|\geq30^{\circ}$）费米-LAT数据，范围从1到10 GeV。 这种方法使我们能够探测单个伽马射线源，并得出一个包含超过98%的费米-LAT第四源目录（4FGL）中列出的所有源的显著伽马射线发射器列表，其流量为$S>3\times10^{-10}\;\mathrm{cm}^{-2}\,\mathrm{s}^{-1}$（大约比4FGL几乎完全覆盖的流量大三倍），在这个流量范围内没有检测到非4FGL源。 此外，我们以参数和非参数形式重建了源计数分布，与先前文献的结果以及我们SBI管道检测到的源之间取得了很大的一致性。 我们还通过异常检测技术定量验证了我们的伽马射线发射模拟器，证明合成数据能很好地再现真实观测的复杂性。 显示英文摘要

摘要： Over the past 16 years, the Fermi Large Area Telescope (LAT) has significantly advanced our view of the GeV gamma-ray sky, yet several key questions remain - such as the composition of the isotropic gamma-ray background, the origin of the Fermi Bubbles or the potential presence of signatures from exotic physics like dark matter. Addressing these challenges requires sophisticated astrophysical modeling and robust statistical methods capable of handling high-dimensional parameter spaces. In this work, we analyze 14 years of high-latitude ($|b|\geq30^{\circ}$) Fermi-LAT data in the range from 1 to 10 GeV using simulation-based inference (SBI) via neural ratio estimation. This approach allows us to detect individual gamma-ray sources and derive a list of significant gamma-ray emitters containing more than 98\% of all sources listed in the Fermi-LAT Fourth Source Catalog (4FGL) with a flux $S>3\times10^{-10}\;\mathrm{cm}^{-2}\,\mathrm{s}^{-1}$ (about a factor of three larger than the flux above which 4FGL is nearly complete), without any non-4FGL source detected in that flux range. Additionally, we reconstruct the source-count distribution in both parametric and non-parametric forms, achieving large agreement with previous literature results as well as those sources detected by our SBI pipeline. We also quantitatively validate our gamma-ray emission simulator via an anomaly detection technique, demonstrating that the synthetic data closely reproduces the complexity of the real observations.