Passage of particles through matter and the effective straggling-function: High-fidelity accelerated simulation via Physics-Informed Machine Learning
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917434663895040 |
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| author | Borysov, Oleksandr Dover, Rotem Gross, Eilam Kakati, Nilotpal Hod, Noam Tal |
| author_facet | Borysov, Oleksandr Dover, Rotem Gross, Eilam Kakati, Nilotpal Hod, Noam Tal |
| contents | High-fidelity simulation of particle-matter interactions provides the essential theoretical reference for diverse physics disciplines, yet generating synthetic datasets at the scale of current and future experiments has become prohibitive. Here, we introduce PHIN-GAN, a novel physics-informed generative adversarial network designed to address this challenge. We derive a set of analytical probability density functions, that effectively describe the ``straggling function'' identified with Landau. For the first time, this enables their continuous evaluation across the entire phase-space. These analytical forms are leveraged to enforce a parametric distribution-level learning objective. Rooted in first principles, PHIN-GAN offers a generalizable, interpretable and scalable proof-of-concept approach for a lossless generative model that maintains the high fidelity of the standard-bearer for simulating such interactions, namely GEANT4, at a fraction of the computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22993 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Passage of particles through matter and the effective straggling-function: High-fidelity accelerated simulation via Physics-Informed Machine Learning Borysov, Oleksandr Dover, Rotem Gross, Eilam Kakati, Nilotpal Hod, Noam Tal High Energy Physics - Experiment High Energy Physics - Phenomenology Computational Physics High-fidelity simulation of particle-matter interactions provides the essential theoretical reference for diverse physics disciplines, yet generating synthetic datasets at the scale of current and future experiments has become prohibitive. Here, we introduce PHIN-GAN, a novel physics-informed generative adversarial network designed to address this challenge. We derive a set of analytical probability density functions, that effectively describe the ``straggling function'' identified with Landau. For the first time, this enables their continuous evaluation across the entire phase-space. These analytical forms are leveraged to enforce a parametric distribution-level learning objective. Rooted in first principles, PHIN-GAN offers a generalizable, interpretable and scalable proof-of-concept approach for a lossless generative model that maintains the high fidelity of the standard-bearer for simulating such interactions, namely GEANT4, at a fraction of the computational cost. |
| title | Passage of particles through matter and the effective straggling-function: High-fidelity accelerated simulation via Physics-Informed Machine Learning |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology Computational Physics |
| url | https://arxiv.org/abs/2604.22993 |