# Application of Machine Learning to the Particle Identification of GAPS

## TL;DR

This paper explores a machine learning approach using deep neural networks to improve particle identification in the GAPS experiment, aiming to enhance detection of rare cosmic-ray antiparticles like antideuterons.

## Contribution

It introduces a novel machine learning method for particle identification in GAPS, complementing traditional likelihood-based techniques and demonstrating its potential through exploratory results.

## Key findings

- Deep learning shows promise for particle identification in GAPS.
- The approach can uncover unknown patterns in antiparticle event data.
- Preliminary results suggest improved identification accuracy.

## Abstract

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.