  GAPS is an international balloon-borne project that contributes to solving
the dark-matter mystery through a highly sensitive survey of cosmic-ray
antiparticles, especially undiscovered antideuterons. To achieve a sufficient
sensitivity to rare antideuterons, a novel particle identification method based
on exotic atom capture and decay has been developed. In parallel to utilizing
this unique event signature in a conventional likelihood-based event
identification scheme, we have begun investigating a complementary approach
using a machine learning technique. In this new approach, a deep-learning
package is trained on a large amount of input data from simulated antiparticle
events through a multi-layered neural network. By applying this unbiased
approach, we expect to mine unknown patterns and give feedback to the
conventional method. In this paper, we report results from exploratory
investigations that illustrate the promise of this new approach.
