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

Siddharth Mishra-Sharma; Kyle Cranmer

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arXiv · 2010.10450

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

Siddharth Mishra-Sharma·Kyle Cranmer

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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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Siddharth Mishra-Sharma, Kyle Cranmer. 2020-10-20. Semi-parametric $\gamma$-ray modeling with Gaussian processes and variational inference. https://arxiv.org/abs/2010.10450

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