SciPost Submission Page
Particle Identification with MLPs and PINNs Using HADES Data
by Marvin Kohls
Submission summary
| Authors (as registered SciPost users): | Marvin Kohls |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.17685v2 (pdf) |
| Date submitted: | Nov. 18, 2025, 8:56 a.m. |
| Submitted by: | Marvin Kohls |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
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| Approach: | Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
Spelling and grammar corrections
Help in rephrasing the explanation about the loss function in order to stay within the 4-page limit
Abstract
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.