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Galactic Center Excess in a New Light: Disentangling the γ-Ray Sky with Bayesian Graph Convolutional Neural Networks.

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Bayesian neural networks can distinguish between pointlike and smooth origins for the Galactic Center excess (GCE). The method favors a smooth GCE, suggesting dark matter (DM) as a potential source.

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Area of Science:

  • Astrophysics
  • Particle Physics
  • Machine Learning

Background:

  • The Galactic Center excess (GCE) is a puzzling gamma-ray emission from the inner Milky Way.
  • Its origin is debated: pointlike sources like millisecond pulsars or a smooth distribution of annihilating dark matter (DM).

Purpose of the Study:

  • To investigate whether Bayesian neural networks (NNs) can resolve the origin of the GCE.
  • To differentiate between pointlike and smooth emission components in astrophysical data.

Main Methods:

  • Development and application of Bayesian neural networks (NNs) for analyzing gamma-ray data.
  • Testing the NN's ability to predict flux fractions from simulated emission components.
  • Applying the trained NN to the Fermi photon-count map of the Galactic Center.

Main Results:

  • The NN accurately predicted flux fractions from simulated components, achieving an average precision of ~0.5%.
  • When applied to Fermi data, the NN identified the GCE as predominantly smooth.
  • Background template estimates were consistent with previous studies.

Conclusions:

  • Bayesian neural networks offer a powerful tool for resolving debates in astrophysics, such as the GCE origin.
  • The findings support a smooth GCE, consistent with a dark matter annihilation hypothesis.
  • This method has broad applicability for analyzing complex astrophysical datasets.