Inference of neutrino flavor evolution through data assimilation and neural differential equations

@article{Rrapaj2020InferenceON,
  title={Inference of neutrino flavor evolution through data assimilation and neural differential equations},
  author={Ermal Rrapaj and Amol V. Patwardhan and Eve Armstrong and George M. Fuller},
  journal={arXiv: High Energy Astrophysical Phenomena},
  year={2020}
}
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… 
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