2016/12/31 by Eve Armstrong, Amol V. Patwardhan, Lucas Johns +3
Mathematics · Physics and Astronomy · #Algorithm #Applied mathematics #Astrophysics #Astrophysics and Cosmic Phenomena #Computer science #Data assimilation #Geometry #Mathematical optimization #Mathematics #Neutrino #Neutrino Physics Research #Nonlinear system #Optimization problem #Parameter space #Particle physics #Particle physics theoretical and experimental studies #Physics #Statistical physics #Supernova #astro-ph.HE #hep-ph
paper · pdf · doi:10.1103/physrevd.96.083008
published as Phys. Rev. D 96, 083008 (2017) · 26 pages, 4 figures
arxiv created 2017/08/17 · openalex publication_date 2017/10/10 · arxiv updated 2017/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We assess the utility of an optimization-based data assimilation (D.A.) technique for treating the problem of nonlinear neutrino flavor transformation in core-collapse supernovae. D.A. uses measurements obtained from a physical system to estimate the state variable evolution and parameter values of the associated model. Formulated as an optimization procedure, D.A. can offer an integration-blind approach to predicting model evolution, which offers an advantage for models that thwart solution via traditional numerical integration techniques. Further, D.A. performs most optimally for models whose equations of motion are nonlinearly coupled. In this exploratory work, we consider a simple steady-state model with two monoenergetic neutrino beams coherently interacting with each other and a background medium. As this model can be solved via numerical integration, we have an independent consistency check for D.A. solutions. We find that the procedure can capture key features of flavor evolution over the entire trajectory, even given measurements of neutrino flavor only at the endpoint, and with an assumed known initial flavor distribution. Further, the procedure permits an examination of the sensitivity of flavor evolution to estimates of unknown model parameters, locates degeneracies in parameter space, and can identify the specific measurements required to break those degeneracies.