26 research outputs found
Analyzing {\gamma}-rays of the Galactic Center with Deep Learning
We present a new method to interpret the -ray data of our inner
Galaxy as measured by the Fermi Large Area Telescope (Fermi LAT). We train and
test convolutional neural networks with simulated Fermi-LAT images based on
models tuned to real data. We use this method to investigate the origin of an
excess emission of GeV -rays seen in previous studies. Interpretations
of this excess include rays created by the annihilation of dark matter
particles and rays originating from a collection of unresolved point
sources, such as millisecond pulsars. Our new method allows precise
measurements of the contribution and properties of an unresolved population of
-ray point sources in the interstellar diffuse emission model.Comment: 24 pages, 11 figure