Galactic Center Excess in a New Light: Disentangling the γ-Ray Sky with Bayesian Graph Convolutional Neural Networks.
A fundamental question regarding the Galactic Center excess (GCE) is whether the underlying structure is pointlike or smooth, often framed in terms of a millisecond pulsar or annihilating dark matter (DM) origin for the emission. We show that Bayesian neural networks (NNs) have the potential to resolve this debate. In simulated data, the method is able to predict the flux fractions from inner Galaxy emission components to on average ∼0.5%. When applied to the Fermi photon-count map, the NN identifies a smooth GCE in the data, suggestive of the presence of DM, with the estimates for the background templates being consistent with existing results.
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Galactic Center Excess in a New Light: Disentangling the γ-Ray Sky with Bayesian Graph Convolutional Neural Networks.
Semantic Scholar · Physics · 2020
Abstract
A fundamental question regarding the Galactic Center excess (GCE) is whether the underlying structure is pointlike or smooth, often framed in terms of a millisecond pulsar or annihilating dark matter (DM) origin for the emission. We show that Bayesian neural networks (NNs) have the potential to resolve this debate. In simulated data, the method is able to predict the flux fractions from inner Galaxy emission components to on average ∼0.5%. When applied to the Fermi photon-count map, the NN identifies a smooth GCE in the data, suggestive of the presence of DM, with the estimates for the background templates being consistent with existing results.