Application of Geometric Deep Learning for Tracking of Hyperons in a Straw Tube Detector

Abstract We present track reconstruction algorithms based on deep learning, tailored to overcome specific central challenges in the field of hadron physics. Two approaches are used: (i) deep learning (DL) model known as fully-connected neural networks (FCNs), and (ii) a geometric deep learning (GDL) model known as graph neural networks (GNNs). The models have been implemented to reconstruct signals in the non-Euclidean detector geometry of the future antiproton experiment PANDA. In particular, the GDL model shows promising results for cases where other, more conventional track-finders fall short: (i) tracks from low-momentum particles that frequently occur in hadron physics experiments and (ii) tracks from long-lived particles such as hyperons, hence originating far from the beam-target interaction point. Benchmark studies using Monte Carlo simulated data from PANDA yield an average technical reconstruction efficiency of 92.6% for high-multiplicity muon events, and 97.1% for the $$\Lambda$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Λ</mml:mi> </mml:math> daughter particles in the reaction $$\bar{p}p \rightarrow \bar{\Lambda }\Lambda \rightarrow \bar{p}\pi ^+ p\pi ^-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> <mml:mi>p</mml:mi> <mml:mo>→</mml:mo> <mml:mover> <mml:mrow> <mml:mi>Λ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> <mml:mi>Λ</mml:mi> <mml:mo>→</mml:mo> <mml:mover> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>+</mml:mo> </mml:msup> <mml:mi>p</mml:mi> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>-</mml:mo> </mml:msup> </mml:mrow> </mml:math> . Furthermore, the technical tracking efficiency is found to be larger than 70% even for particles with transverse momenta $$p_\text {T}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>p</mml:mi> <mml:mtext>T</mml:mtext> </mml:msub> </mml:math> below 100 MeV/ c . For the long-lived $$\Lambda$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Λ</mml:mi> </mml:math> hyperons, the track reconstruction efficiency is fairly independent of the distance between the beam-target interaction point and the $$\Lambda$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Λ</mml:mi> </mml:math> decay vertex. This underlines the potential of machine-learning-based tracking, also for experiments at low- and intermediate-beam energies.