论文标题
GCE在新的角度：与贝叶斯图卷积神经网络拆开$γ$ ray的天空
The GCE in a New Light: Disentangling the $γ$-ray Sky with Bayesian Graph Convolutional Neural Networks
论文作者
论文摘要
关于银河中心过剩（GCE）的一个基本问题是基础结构是类似点状还是光滑。这场辩论通常以毫秒的脉冲星或歼灭的暗物质（DM）起源而构成，等待确定的解决方案。在这项工作中，我们使用贝叶斯图卷积神经网络处理了问题。在模拟数据中，我们的神经网络（NN）能够将内部星系发射组件的通量重建为平均$ \ sim $ 0.5％，可与非波斯顿模板拟合（NPTF）相当。当应用于实际的$ \ textit {fermi} $ - lat数据时，我们发现来自背景模板的磁通级数的NN估计与NPTF一致；但是，GCE几乎完全归因于光滑的发射。虽然暗示性，但我们并没有为GCE提供明确的分辨率，因为NN倾向于低估点 - 源的峰值的通量，而在1 $σ$检测阈值附近。然而，该技术对许多系统学表现出鲁棒性，包括重建注射的DM，弥漫性不隔底层和未建模的南北不对称。因此，尽管NN正在暗示目前GCE的平稳起源，但通过进一步的改进，我们认为贝叶斯深度学习是可以很好地解决这个DM的谜团。
A fundamental question regarding the Galactic Center Excess (GCE) is whether the underlying structure is point-like or smooth. This debate, often framed in terms of a millisecond pulsar or annihilating dark matter (DM) origin for the emission, awaits a conclusive resolution. In this work we weigh in on the problem using Bayesian graph convolutional neural networks. In simulated data, our neural network (NN) is able to reconstruct the flux of inner Galaxy emission components to on average $\sim$0.5%, comparable to the non-Poissonian template fit (NPTF). When applied to the actual $\textit{Fermi}$-LAT data, we find that the NN estimates for the flux fractions from the background templates are consistent with the NPTF; however, the GCE is almost entirely attributed to smooth emission. While suggestive, we do not claim a definitive resolution for the GCE, as the NN tends to underestimate the flux of point-sources peaked near the 1$σ$ detection threshold. Yet the technique displays robustness to a number of systematics, including reconstructing injected DM, diffuse mismodeling, and unmodeled north-south asymmetries. So while the NN is hinting at a smooth origin for the GCE at present, with further refinements we argue that Bayesian Deep Learning is well placed to resolve this DM mystery.
