  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.
