A Neural-Network Framework for Tracking and Identification of Cosmic-Ray Nuclei in the RadMap Telescope

TL;DR

This paper introduces a neural-network framework for reconstructing cosmic-ray nuclei properties in the RadMap Telescope, achieving high accuracy in trajectory, charge, and energy measurements using simulated data.

Contribution

The work presents a novel neural-network approach combined with Geant4 simulations for precise cosmic-ray nuclei tracking and identification in space-based detectors.

Findings

Trajectory angular resolution better than 1.4°

Charge separation accuracy over 95% for nuclei with Z≤8

Energy resolution below 20% for energies under 1 GeV/n and elements up to iron

Abstract

We present a neural-network framework designed to reconstruct the properties of cosmic-ray nuclei traversing the scintillating-fiber tracking calorimeter of the RadMap Telescope. Employing the Geant4 simulation toolkit and a simplified model of the detector to generate training and test data, we achieve the spectroscopic capabilities required for an accurate determination of the biologically relevant dose that astronauts receive in space. We can reconstruct a particle's trajectory with an angular resolution of better than $1.4^\circ$ and achieve a charge separation of better than $95\%$ for nuclei with $Z\leq8$; specifically, we reach an accuracy of $99.8\%$ for hydrogen. The energy resolution is $<20\%$ for energies below 1 GeV/n and elements up to iron. We also discuss the limitations of our detector, the reconstruction framework, and this feasibility study, as well as possible improvements.