Semi-parametric $γ$-ray modeling with Gaussian processes and variational inference
This addresses the challenge of accurately characterizing gamma-ray data for astrophysicists, especially in dark matter searches, but appears incremental as it builds on existing statistical methods.
The paper tackled the problem of mismodeling diffuse Galactic gamma-ray emission, which can bias astrophysical data interpretation, by introducing Gaussian processes and variational inference to build flexible background and signal models, enabling more robust analysis of gamma-ray sky composition, particularly for dark matter signals in the Galactic Center.
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.