Particle Identification with MLPs and PINNs Using HADES Data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918205068410880 |
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| author | Kohls, Marvin |
| author_facet | Kohls, Marvin |
| contents | In experimental nuclear and particle physics, the extraction of high-purity samples of rare events critically depends on the efficiency and accuracy of particle identification (PID). In this work, we present a PID method applied to HADES data at the level of fully reconstructed particle track candidates. The results demonstrate a significant improvement in PID performance compared to conventional techniques, highlighting the potential of physics-informed neural networks as a powerful tool for future data analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Particle Identification with MLPs and PINNs Using HADES Data Kohls, Marvin Data Analysis, Statistics and Probability Nuclear Experiment In experimental nuclear and particle physics, the extraction of high-purity samples of rare events critically depends on the efficiency and accuracy of particle identification (PID). In this work, we present a PID method applied to HADES data at the level of fully reconstructed particle track candidates. The results demonstrate a significant improvement in PID performance compared to conventional techniques, highlighting the potential of physics-informed neural networks as a powerful tool for future data analyses. |
| title | Particle Identification with MLPs and PINNs Using HADES Data |
| topic | Data Analysis, Statistics and Probability Nuclear Experiment |
| url | https://arxiv.org/abs/2509.17685 |