Antimatter Annihilation Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber

TL;DR

This paper introduces PEAR, a deep learning ensemble model based on PointNet, that directly reconstructs annihilation vertices in the ALPHA-g experiment's rTPC, outperforming traditional track-fitting methods especially in challenging cases.

Contribution

The paper presents a novel deep learning approach for vertex reconstruction that bypasses track fitting, improving accuracy and robustness in the ALPHA-g experiment.

Findings

PEAR outperforms standard methods in simulated data.

PEAR accurately reconstructs vertices where traditional methods fail.

Deep learning enhances vertex reconstruction robustness.

Abstract

The ALPHA-g experiment at CERN aims to precisely measure the terrestrial gravitational acceleration of antihydrogen atoms. A radial Time Projection Chamber (rTPC), that surrounds the ALPHA-g magnetic trap, is employed to determine the annihilation location, called the vertex. The standard approach requires identifying the trajectories of the ionizing particles in the rTPC from the location of their interaction in the gas (spacepoints), and inferring the vertex positions by finding the point where those trajectories (helices) pass closest to one another. In this work, we present a novel approach to vertex reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR), directly learns the relation between the location of the vertices and the rTPC spacepoints, thus eliminating the need to identify and fit the particle tracks. PEAR shows strong performance in reconstructing vertical vertex positions from simulated data, that is superior to the standard approach for all metrics considered. Furthermore, the deep learning approach can reconstruct the vertical vertex position when the standard approach fails.