Generative adversarial neural networks for simulating neutrino interactions
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915360754630656 |
|---|---|
| author | Bonilla, Jose L. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Kowal, Beata E. Prasad, Hemant Sobczyk, Jan T. |
| author_facet | Bonilla, Jose L. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Kowal, Beata E. Prasad, Hemant Sobczyk, Jan T. |
| contents | We propose a new approach to simulate neutrino scattering events as an alternative to the standard Monte Carlo generator approach. Generative adversarial neural network (GAN) models are developed to simulate charged current neutrino-carbon collisions in the few-GeV energy range. We consider a simplified framework to generate muon kinematic variables, specifically its energy and scattering angle. GAN models are trained on simulation data from \nuwro{} Monte Carlo event generator. Two GAN models have been obtained: one simulating quasielastic neutrino-nucleus scatterings and another simulating all interactions at given neutrino energy. The models work for neutrino energy ranging from 300 MeV to 10 GeV. The performance of both models has been assessed using two statistical metrics. It is shown that both GAN models successfully reproduce the distribution of muon kinematics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20244 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative adversarial neural networks for simulating neutrino interactions Bonilla, Jose L. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Kowal, Beata E. Prasad, Hemant Sobczyk, Jan T. High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Nuclear Experiment Nuclear Theory We propose a new approach to simulate neutrino scattering events as an alternative to the standard Monte Carlo generator approach. Generative adversarial neural network (GAN) models are developed to simulate charged current neutrino-carbon collisions in the few-GeV energy range. We consider a simplified framework to generate muon kinematic variables, specifically its energy and scattering angle. GAN models are trained on simulation data from \nuwro{} Monte Carlo event generator. Two GAN models have been obtained: one simulating quasielastic neutrino-nucleus scatterings and another simulating all interactions at given neutrino energy. The models work for neutrino energy ranging from 300 MeV to 10 GeV. The performance of both models has been assessed using two statistical metrics. It is shown that both GAN models successfully reproduce the distribution of muon kinematics. |
| title | Generative adversarial neural networks for simulating neutrino interactions |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2502.20244 |