# Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

arXiv: https://arxiv.org/abs/1706.01826 · HTML: https://mlsanity.com/paper/1706.01826/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Antimatter Annihilation Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber](https://mlsanity.com/paper/2502.12169/) (2025)

## Abstract (arXiv)

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).