• Corpus ID: 224803577

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

@article{MishraSharma2020SemiparametricM,
title={Semi-parametric \$\gamma\$-ray modeling with Gaussian processes and variational inference},
author={Siddharth Mishra-Sharma and Kyle Cranmer},
journal={arXiv: High Energy Astrophysical Phenomena},
year={2020}
}
• Published 20 October 2020
• Physics, Mathematics
• arXiv: High Energy Astrophysical Phenomena
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…
2 Citations

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