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

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

Siddharth Mishra-Sharma
(
- New York U.
)
,
Kyle Cranmer
(
- New York U.
)

Oct 20, 2020

8 pages

Contribution to:

- NeurIPS 2020

e-Print:

2010.10450 [astro-ph.HE]

View in:

ADS Abstract Service

reference search10 citations

Citations per year

Abstract: (arXiv)

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.

Note:

8 pages, 1 figure, extended abstract submitted to the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020

gamma ray: emission
variational
galaxy
dark matter: parametrization
background
photon
GeV

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