# Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

## TL;DR

This paper introduces a deep learning approach for detecting antihydrogen annihilation events, significantly improving detection efficiency and accuracy over traditional methods in antimatter physics experiments at CERN.

## Contribution

It presents a novel deep learning technique for antihydrogen detection, outperforming traditional track and vertex reconstruction methods in efficiency and accuracy.

## Key findings

- Deep learning triples event coverage.
- Over 5% improvement in AUC performance.
- Enhanced detection efficiency in antimatter experiments.

## Abstract

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).