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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\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\Lambda$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u039b<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    daughter particles in the reaction\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\bar{p}p \\rightarrow \\bar{\\Lambda }\\Lambda \\rightarrow \\bar{p}\\pi ^+ p\\pi ^-$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mover>\n                              <mml:mrow>\n                                <mml:mi>p<\/mml:mi>\n                              <\/mml:mrow>\n                              <mml:mrow>\n                                <mml:mo>\u00af<\/mml:mo>\n                              <\/mml:mrow>\n                            <\/mml:mover>\n                            <mml:mi>p<\/mml:mi>\n                            <mml:mo>\u2192<\/mml:mo>\n                            <mml:mover>\n                              <mml:mrow>\n                                <mml:mi>\u039b<\/mml:mi>\n                              <\/mml:mrow>\n                              <mml:mrow>\n                                <mml:mo>\u00af<\/mml:mo>\n                              <\/mml:mrow>\n                            <\/mml:mover>\n                            <mml:mi>\u039b<\/mml:mi>\n                            <mml:mo>\u2192<\/mml:mo>\n                            <mml:mover>\n                              <mml:mrow>\n                                <mml:mi>p<\/mml:mi>\n                              <\/mml:mrow>\n                              <mml:mrow>\n                                <mml:mo>\u00af<\/mml:mo>\n                              <\/mml:mrow>\n                            <\/mml:mover>\n                            <mml:msup>\n                              <mml:mi>\u03c0<\/mml:mi>\n                              <mml:mo>+<\/mml:mo>\n                            <\/mml:msup>\n                            <mml:mi>p<\/mml:mi>\n                            <mml:msup>\n                              <mml:mi>\u03c0<\/mml:mi>\n                              <mml:mo>-<\/mml:mo>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    . Furthermore, the technical tracking efficiency is found to be larger than 70% even for particles with transverse momenta\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$p_\\text {T}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mi>p<\/mml:mi>\n                            <mml:mtext>T<\/mml:mtext>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    below 100 MeV\/\n                    <jats:italic>c<\/jats:italic>\n                    . For the long-lived\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\Lambda$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u039b<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    hyperons, the track reconstruction efficiency is fairly independent of the distance between the beam-target interaction point and the\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\Lambda$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u039b<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    decay vertex. This underlines the potential of machine-learning-based tracking, also for experiments at low- and intermediate-beam energies.\n                  <\/jats:p>","DOI":"10.1007\/s41781-025-00146-3","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:38:43Z","timestamp":1761061123000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Geometric Deep Learning for Tracking of Hyperons in a Straw Tube Detector"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0198-5852","authenticated-orcid":false,"given":"Adeel","family":"Akram","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9745-1638","authenticated-orcid":false,"given":"Xiangyang","family":"Ju","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0990-3145","authenticated-orcid":false,"given":"Michael","family":"Papenbrock","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9770-7488","authenticated-orcid":false,"given":"Jenny","family":"Taylor","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6665-0095","authenticated-orcid":false,"given":"Tobias","family":"Stockmanns","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3490-9584","authenticated-orcid":false,"given":"Karin","family":"Sch\u00f6nning","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"146_CR1","unstructured":"Lutz MFM, et al (2009) Physics Performance Report for PANDA: Strong Interaction Studies with Antiprotons. arXiv:0903.3905 [hep-ex]"},{"key":"146_CR2","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1038\/s41567-019-0494-8","volume":"15","author":"M Ablikim","year":"2019","unstructured":"Ablikim M et al (2019) Polarization and Entanglement in Baryon-Antibaryon Pair Production in Electron-Positron Annihilation. 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