用生成对抗网络模拟中微子与碳核散射，提升仿真效率。
Generative adversarial neural networks for simulating neutrino interactions
- 采用GAN模型生成μ子能量与散射角分布，替代传统蒙特卡洛方法。
- 在300 MeV至10 GeV范围内，模型准确复现μ子动量分布。
- 适用于中微子实验仿真，尤其适合需要快速生成的场景。
我们提出一种新方法，用于模拟中微子散射事件，作为标准蒙特卡洛生成器的替代方案。针对300 MeV至10 GeV能量范围内的带电流中微子-碳碰撞，构建了生成对抗神经网络（GAN）模型，专门生成μ子的运动学变量，包括其能量和散射角。模型基于 nuwro{}蒙特卡洛事件生成器的仿真数据进行训练。获得两种GAN模型：一种用于模拟准弹性中微子-核散射，另一种用于模拟特定中微子能量下的所有相互作用。通过两种统计指标评估性能，结果显示两种模型均成功重现了μ子运动学分布。
原文摘要 · Abstract (English)
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.
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