  The Advanced Energetic Pair Telescope gamma-ray polarimeter uses a time
projection chamber for measuring pair production events and is expected to
generate a raw instrument data rate four orders of magnitude greater than is
transmittable with typical satellite data communications. GammaNet, a
convolutional neural network, proposes to solve this problem by performing
event classification on-board for pair production and background events,
reducing the data rate to a level that can be accommodated by typical satellite
communication systems. In order to train GammaNet, a set of 1.1x10^6 pair
production events and 10^6 background events were simulated for the Advanced
Energetic Pair Telescope using the Geant4 Monte Carlo code. An additional set
of 10^3 pair production and 10^5 background events were simulated to test
GammaNet's capability for background discrimination. With optimization,
GammaNet has achieved the proposed background rejection requirements for
Galactic Cosmic Ray proton events. Given the best case assumption for downlink
speeds, signal sensitivity for pair production ranged between 1.1 +/- 0.5% to
69 +/- 2% for 5 and 250 MeV incident gamma rays. This range became 0.1 +/- 0.1%
to 17 +/- 2% for the worst case scenario of downlink speeds. The application of
a feature visualization algorithm to GammaNet demonstrated decreased response
to electronic noise and events exiting or entering the frame and increased
response to parallel tracks that are close in proximity. GammaNet has been
successfully implemented and shows promising results.
