Galactic Center Excess in a New Light: Disentangling the $\ensuremath{\gamma}$-Ray Sky with Bayesian Graph Convolutional Neural Networks

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

Florian List, Nicholas L. Rodd, Geraint F. Lewis, and Ishaan Bhat

Phys. Rev. Lett. 125, 241102 – Published 10 December 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 $\sim 0.5 %$ . 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.

Received 29 June 2020
Revised 24 September 2020
Accepted 26 October 2020

DOI:https://doi.org/10.1103/PhysRevLett.125.241102

Physics Subject Headings (PhySH)

Dark matter Gamma ray astronomy Particle dark matter

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Florian List ^1,*, Nicholas L. Rodd ^2,3, Geraint F. Lewis ¹, and Ishaan Bhat ⁴

¹Sydney Institute for Astronomy, School of Physics, A28, The University of Sydney, New South Wales 2006, Australia
²Berkeley Center for Theoretical Physics, University of California, Berkeley, California 94720, USA
³Theoretical Physics Group, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
⁴UMC Utrecht, Image Sciences Institute, 3508 GA Utrecht, The Netherlands

^*florian.list@sydney.edu.au

Extracting the Galactic Center excess’ source-count distribution with neural nets

Florian List, Nicholas L. Rodd, and Geraint F. Lewis

Phys. Rev. D 104, 123022 (2021)

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Vol. 125, Iss. 24 — 11 December 2020

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Physical Review Letters

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

Florian List, Nicholas L. Rodd, Geraint F. Lewis, and Ishaan Bhat

Phys. Rev. Lett. 125, 241102 – Published 10 December 2020

Abstract

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Extracting the Galactic Center excess’ source-count distribution with neural nets

Florian List, Nicholas L. Rodd, and Geraint F. Lewis

Phys. Rev. D 104, 123022 (2021)

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Physical Review Letters

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

Florian List, Nicholas L. Rodd, Geraint F. Lewis, and Ishaan Bhat

Phys. Rev. Lett. 125, 241102 – Published 10 December 2020

Abstract

Physics Subject Headings (PhySH)

Authors & Affiliations

See Also

Extracting the Galactic Center excess’ source-count distribution with neural nets

Florian List, Nicholas L. Rodd, and Geraint F. Lewis

Phys. Rev. D 104, 123022 (2021)

Article Text (Subscription Required)

Supplemental Material (Subscription Required)

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