Semi-parametric $\gamma$-ray modeling with Gaussian processes and variational inference

• URL: http://arxiv.org/abs/2010.10450v1
• Date: Tue, 20 Oct 2020 17:04:37 GMT
• Title: Semi-parametric $\gamma$-ray modeling with Gaussian processes and variational inference
• Authors: Siddharth Mishra-Sharma and Kyle Cranmer
• Abstract summary: Mismodeling the uncertain, diffuse emission of Galactic origin can seriously bias the characterization of astrophysical gamma-ray data. We introduce a novel class of methods that use Gaussian processes and variational inference to build flexible background and signal models for gamma-ray analyses.
• Score: 9.405199445496429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mismodeling the uncertain, diffuse emission of Galactic origin can seriously bias the characterization of astrophysical gamma-ray data, particularly in the region of the Inner Milky Way where such emission can make up over 80% of the photon counts observed at ~GeV energies. We introduce a novel class of methods that use Gaussian processes and variational inference to build flexible background and signal models for gamma-ray analyses with the goal of enabling a more robust interpretation of the make-up of the gamma-ray sky, particularly focusing on characterizing potential signals of dark matter in the Galactic Center with data from the Fermi telescope.

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