用神经网络联合分析光谱与空间数据，发现银河系中心过剩可能源于大量暗源而非少数亮源。
On the Energy Distribution of the Galactic Center Excess' Sources
- 引入神经网络模拟推断法，同时处理能量分布与空间结构数据。
- 点源数量中位数达10万级，90%置信下超过3.5万，远超以往估计。
- 结果支持暗物质解释，或需极多稀疏源，适合关注宇宙射线与暗物质的学者。
银河系中心过剩（GCE）可能预示着湮灭暗物质的发现。然而，已有分析指出该发射结构内存在微弱点源，而这些研究仅依赖空间信息，丢弃了可区分过剩与天体背景的光谱信息。本文展示一种基于神经网络模拟推断的方法，能联合分析空间与能量数据。加入能量信息后，疑似点源显著变暗：若为点源，则其数量中位数为10⁵量级，90%置信区间下超过35,000个，比早期分析结果高两个数量级；若考虑背景系统误差，数量可减少约一个数量级。在最优背景模型下，过剩几乎符合暗物质预期的泊松辐射特征。
原文摘要 · Abstract (English)
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.
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