Semi-parametric $gamma$-ray modeling with Gaussian processes and variational inference. (arXiv:2010.10450v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Mishra_Sharma_S/0/1/0/all/0/1">Siddharth Mishra-Sharma</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cranmer_K/0/1/0/all/0/1">Kyle Cranmer</a>
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.
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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