Semi-parametric $\gamma$-ray modeling with Gaussian processes and variational inference
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.
Cite
@article{arxiv.2010.10450,
title = {Semi-parametric $\gamma$-ray modeling with Gaussian processes and variational inference},
author = {Siddharth Mishra-Sharma and Kyle Cranmer},
journal= {arXiv preprint arXiv:2010.10450},
year = {2020}
}
Comments
8 pages, 1 figure, extended abstract submitted to the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020