论文标题
通过数据同化和神经微分方程的推断中微子风味演变的推断
Inference of neutrino flavor evolution through data assimilation and neural differential equations
论文作者
论文摘要
中微子风味在密集的环境中的演变，例如核心折叠超新星和二元紧凑型物体合并构成了一个重要且未解决的问题。它的解决方案对这些环境中的动力学和重元素核合成具有潜在的影响。在本文中，我们基于最近的工作，以探索基于推理的技术来估计模型参数和中微子风味演化历史。我们结合了数据同化，普通的微分方程求解器和神经网络，以制作针对非线性动力学系统量身定制的推理方法。使用此体系结构以及一个简单的两中性，两种风味的模型，我们在四个实验设置的帮助下测试了各种优化算法。我们发现，采用这种新体系结构以及进化优化算法，可以准确捕获四个实验中的风味史。这项工作为将推理技术扩展到大量中微子提供了更多选择。
The evolution of neutrino flavor in dense environments such as core-collapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavy-element nucleosynthesis in these environments. In this paper, we build upon recent work to explore inference-based techniques for estimation of model parameters and neutrino flavor evolution histories. We combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for non-linear dynamical systems. Using this architecture, and a simple two-neutrino, two-flavor model, we test various optimization algorithms with the help of four experimental setups. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the four experiments. This work provides more options for extending inference techniques to large numbers of neutrinos.
