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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2503.14305 |
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| _version_ | 1866915203703111680 |
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| author | Akram, Adeel Ju, Xiangyang Papenbrock, Michael Taylor, Jenny Stockmanns, Tobias Schönning, Karin |
| author_facet | Akram, Adeel Ju, Xiangyang Papenbrock, Michael Taylor, Jenny Stockmanns, Tobias Schönning, Karin |
| contents | 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 a 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 $Λ$ daughter particles in the reaction $\bar{p}p \to \barΛΛ\to \bar{p}π^+ pπ^-$. Furthermore, the technical tracking efficiency is found to be larger than 70% even for particles with transverse momenta $p_T$ below 100 MeV/c. For the long-lived $Λ$ hyperons, the track reconstruction efficiency is fairly independent of the distance between the beam-target interaction point and the $Λ$ decay vertex. This underlines the potential of machine-learning-based tracking, also for experiments at low- and intermediate-beam energies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Application of Geometric Deep Learning for Tracking of Hyperons in a Straw Tube Detector Akram, Adeel Ju, Xiangyang Papenbrock, Michael Taylor, Jenny Stockmanns, Tobias Schönning, Karin High Energy Physics - Experiment 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 a 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 $Λ$ daughter particles in the reaction $\bar{p}p \to \barΛΛ\to \bar{p}π^+ pπ^-$. Furthermore, the technical tracking efficiency is found to be larger than 70% even for particles with transverse momenta $p_T$ below 100 MeV/c. For the long-lived $Λ$ hyperons, the track reconstruction efficiency is fairly independent of the distance between the beam-target interaction point and the $Λ$ decay vertex. This underlines the potential of machine-learning-based tracking, also for experiments at low- and intermediate-beam energies. |
| title | Application of Geometric Deep Learning for Tracking of Hyperons in a Straw Tube Detector |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2503.14305 |