The Galactic Center Excess (GCE) may yet herald the discovery of annihilating dark matter. Weighing against that conclusion are analyses showing evidence for dim point sources within the spatial structure of the emission. Due to technical limitations these analyses are purely spatial with all spectral information that could disentangle the excess from astrophysical backgrounds discarded. Here, we demonstrate that a neural network simulation-based inference approach can jointly analyze the spatial and spectra data. The addition is profound: energy information drives the putative point sources to be significantly dimmer, indicating either the GCE is truly diffuse in nature or made of an exceptionally large number of sources. Quantitatively, for our best fit background model, the excess is essentially consistent with Poisson emission as predicted by dark matter. If due to point sources, our median prediction is $\mathcal{O}(10^5)$ sources, or more than 35,000 at 90\% confidence, both orders of magnitude larger than the hundreds preferred by earlier point-source analyses of the GCE, although variations allowed by background systematics could reduce the required number of sources by roughly an order of magnitude.

翻译：银河系中心超额发射( GCE )或许预示着湮灭暗物质的发现。然而,与此结论相悖的是,一些分析表明在该发射的空间结构中存在暗淡的点源。由于技术限制,这些分析仅局限于空间维度,而将所有能够将超额发射与天体物理背景分离的能谱信息予以摒弃。在此,我们证明基于神经网络模拟的推理方法可以联合分析空间与能谱数据。这种整合意义深远:能量信息使假定的点源显著变暗,表明GCE要么本质上是弥散的,要么由异常大量的源组成。定量而言,在我们最优拟合的背景模型下,超额发射基本上与暗物质所预言的泊松发射一致。若归因于点源,我们的中值预测约为$\mathcal{O}(10^5)$个源,或90%置信度下超过35,000个源,这两个数量级均远大于早期GCE点源分析所偏好的数百个源,尽管背景系统误差引入的变异可能将所需源数量降低约一个数量级。