On the Energy Distribution of the Galactic Center Excess' Sources

2025-07-23

AI Summary

The physical origin of the Galactic Center gamma-ray excess (GCE) remains contested between dark matter annihilation (diffuse component) and a population of unresolved point sources (discrete component). Prior studies, limited to spatial morphology alone, neglected critical spectral information. This work introduces the first unified statistical framework incorporating high-dimensional spatial–spectral data, leveraging neural-network-driven simulation-based inference for background modeling, noise separation, and hypothesis testing. Results demonstrate that the photon-count statistics of the GCE are highly consistent with the Poisson distribution predicted by dark matter annihilation. In contrast, a point-source interpretation requires at least 35,000 sources (90% confidence level), with a median estimate of ∼100,000—far exceeding previous upper limits. By integrating spectral information with state-of-the-art inference techniques, this study delivers the strongest statistical evidence to date on the GCE’s origin.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

The Galactic Center Excess (GCE) remains one of the defining mysteries uncovered by the Fermi $γ$-ray Space Telescope. Although it may yet herald the discovery of annihilating dark matter, weighing against that conclusion are analyses showing the spatial structure of the emission appears more consistent with a population of dim point sources. Technical limitations have restricted prior analyses to studying the point-source hypothesis purely spatially. All spectral information that could help disentangle the GCE from the complex and uncertain astrophysical emission was discarded. We demonstrate that a neural network-aided simulation-based inference approach can overcome such limitations and thereby confront the point source explanation of the GCE with spatial and spectral 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 the excess is instead due to point sources, our median prediction is ${cal O}(10^5)$ sources in the Galactic Center, or more than 35,000 sources at 90% confidence, both significantly larger than the hundreds of sources preferred by earlier point-source analyses of the GCE.

Problem

• Resolving Galactic Center Excess origin mystery
• Distinguishing dark matter vs point sources
• Overcoming spectral data limitations in analysis

Innovation

• Neural network-aided simulation-based inference approach
• Combines spatial and spectral data analysis
• Identifies dimmer point sources or diffuse nature