  Antihydrogen is at the forefront of antimatter research at the CERN
Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry
and antigravity effects require the efficient detection of antihydrogen
annihilation events, which is performed using highly granular tracking
detectors installed around an antimatter trap. Improving the efficiency of the
antihydrogen annihilation detection plays a central role in the final
sensitivity of the experiments. We propose deep learning as a novel technique
to analyze antihydrogen annihilation data, and compare its performance with a
traditional track and vertex reconstruction method. We report that the deep
learning approach yields significant improvement, tripling event coverage while
simultaneously improving performance by over 5% in terms of Area Under Curve
(AUC).
