  The PANDA (anti-Proton ANnihilation at DArmstadt) experiment at the Facility
for Anti-proton and Ion Research is going to study strong interactions at the
scale at which quarks are confined to form hadrons. A continuous beam of
antiproton, provided by the High Energy Storage Ring (HESR), will impinge on a
fixed hydrogen target. The antiproton beam momentum spans from 1.5 GeV {Natural
units, c=1} to 15 GeV \cite{physics2009report}, will create optimal conditions
for studying many different aspects of hadron physics, including hyperon
physics.
  Precision physics studies require a highly efficient particle track
reconstruction. The Straw Tube Tracker in PANDA is the main component for that
purpose. It has a hexagonal geometry, consisting of 4224 gas-filled tubes
arranged in 26 layers and six sectors. However, the challenge is reconstructing
low momentum charged particles given the complex detector geometry and the
strongly curved particle trajectory. This paper presents the first application
of a geometric deep learning pipeline to track reconstruction in the PANDA
experiment. The pipeline reconstructs more than 95\% of particle tracks and
creates less than 0.3\% fake tracks. The promising results make the pipeline a
strong candidate algorithm for the experiment.
