Machine learning analysis keeps dark matter in the running for Milky Way glow

A new study using machine learning suggests that dark matter could still explain a mysterious gamma-ray glow at the center of the Milky Way. Researchers from the University of Vienna and Lawrence Berkeley National Laboratory reached this conclusion after analyzing more than a million simulated observations.

The Galactic Center Excess has puzzled scientists for over a decade. Previous analyses often pointed to millisecond pulsars as the source, but those studies did not consider the energy of individual gamma-ray photons.

The team trained a machine-learning system that evaluated both the spatial distribution of the signal and photon energies together. Their results indicate that any point sources would have to be extremely faint, making them nearly indistinguishable from dark matter annihilation.

If pulsars were responsible, at least 35,000 would be needed in the region, far more than earlier estimates suggested. Florian List of the University of Vienna noted that the origin remains one of the longest-running debates in astrophysics and that it is still too early to rule out dark matter.

The findings were published in Physical Review Letters. The source of the glow is still unknown.

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Building on prior detections of gamma-ray emissions from the Milky Way's center, physicists led by Gordan Krnjaic at Fermilab propose dark matter consists of two distinct particles that interact to produce detectable signals. This resolves the puzzle of signals in the Milky Way but none in dark-matter-rich dwarf galaxies, as observed by the Fermi Gamma-ray Space Telescope.

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