Events at Physics
technique for treating the problem of nonlinear neutrino flavor
transformation in core-collapse supernovae. D.A. was invented for
numerical weather prediction, and it shares some features of machine
learning for the purposes of predictive power. Within the D.A. framework,
one 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 mono-energetic
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.