Analyzing γ-rays of the Galactic Center with Deep Learning

Abstract

We present a new method to interpret the $\gamma$-ray data of our inner Galaxy as measured by the Fermi Large Area Telescope (Fermi LAT). We train and test convolutional neural networks with simulated Fermi-LAT images based on models tuned to real data. We use this method to investigate the origin of an excess emission of GeV $\gamma$-rays seen in previous studies. Interpretations of this excess include $\gamma$ rays created by the annihilation of dark matter particles and $\gamma$ rays originating from a collection of unresolved point sources, such as millisecond pulsars. Our new method allows precise measurements of the contribution and properties of an unresolved population of $\gamma$-ray point sources in the interstellar diffuse emission model.

24 pages, 11 figures

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Cited by in corpus (25)

• The Fermi-LAT GeV Excess Traces Stellar Mass in the Galactic Bulge
• Gravitational wave signal recognition of O1 data by deep learning
• The GCE in a New Light: Disentangling the $\gamma$-ray Sky with Bayesian Graph Convolutional Neural Networks
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• Galactic Binaries Can Explain the Fermi Galactic Center Excess and 511 keV Emission
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• Dim but not entirely dark: Extracting the Galactic Center Excess' source-count distribution with neural nets
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