Conditional Wasserstein GAN for Simulating Neutrino Event Summaries using Incident Energy of Electron Neutrinos
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910141593419776 |
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| author | S., Dipthi Desikan, Kalyani |
| author_facet | S., Dipthi Desikan, Kalyani |
| contents | Event simulation for electron neutrino interactions plays a foundational role in precision measurements in particle physics experiments, yet the computational demand of traditional Monte Carlo methods remains a significant challenge, especially for complete, high-dimensional event reconstruction. In this study, we present a generative model based on the Conditional Wasserstein Generative Adversarial Network (CW-GAN) framework. This architecture is conditioned on the input neutrino energy. It utilizes a Wasserstein loss function, stabilized by a gradient penalty, to learn the complex mapping from a latent space to structured kinematic data. Our model is tailored to replicate the full multidimensional kinematics of electron neutrino interactions as described by the GENIE event generator. Our focus is specifically on the Inverse Beta Decay (IBD-CC), Neutral Current (NC), and nue-e-elastic scattering processes (NuEElastic), spanning an energy window of 10-31 MeV. Our approach abandons variable reduction schemes and instead generates the entire summary ntuple, enabling holistic event-by-event modeling. Training is performed separately for each of the three interaction types, with rigorous convergence monitoring over 100-300 epochs per channel. We perform a rigorous quantitative validation against held-out GENIE test datasets. The generated samples demonstrate fidelity, reproducing the 1D marginal distributions for all kinematic variables with statistical compatibility, and successfully capturing the complex non-linear correlations between them. This work offers a scalable and efficient alternative to traditional MC event generation, providing full-spectrum kinematic simulation for key electron neutrino interaction channels while drastically reducing computational overhead. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_21599 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditional Wasserstein GAN for Simulating Neutrino Event Summaries using Incident Energy of Electron Neutrinos S., Dipthi Desikan, Kalyani High Energy Physics - Phenomenology 85-08 Event simulation for electron neutrino interactions plays a foundational role in precision measurements in particle physics experiments, yet the computational demand of traditional Monte Carlo methods remains a significant challenge, especially for complete, high-dimensional event reconstruction. In this study, we present a generative model based on the Conditional Wasserstein Generative Adversarial Network (CW-GAN) framework. This architecture is conditioned on the input neutrino energy. It utilizes a Wasserstein loss function, stabilized by a gradient penalty, to learn the complex mapping from a latent space to structured kinematic data. Our model is tailored to replicate the full multidimensional kinematics of electron neutrino interactions as described by the GENIE event generator. Our focus is specifically on the Inverse Beta Decay (IBD-CC), Neutral Current (NC), and nue-e-elastic scattering processes (NuEElastic), spanning an energy window of 10-31 MeV. Our approach abandons variable reduction schemes and instead generates the entire summary ntuple, enabling holistic event-by-event modeling. Training is performed separately for each of the three interaction types, with rigorous convergence monitoring over 100-300 epochs per channel. We perform a rigorous quantitative validation against held-out GENIE test datasets. The generated samples demonstrate fidelity, reproducing the 1D marginal distributions for all kinematic variables with statistical compatibility, and successfully capturing the complex non-linear correlations between them. This work offers a scalable and efficient alternative to traditional MC event generation, providing full-spectrum kinematic simulation for key electron neutrino interaction channels while drastically reducing computational overhead. |
| title | Conditional Wasserstein GAN for Simulating Neutrino Event Summaries using Incident Energy of Electron Neutrinos |
| topic | High Energy Physics - Phenomenology 85-08 |
| url | https://arxiv.org/abs/2603.21599 |