The multi-messenger astrophysics of compact objects presents a vast range of environments where neutrino flavor transformation may occur and may be important for nucleosynthesis, dynamics, and a detected neutrino signal. Development of efficient techniques for surveying flavor evolution solution spaces in these diverse environments, and which augment and complement existing sophisticated computational tools, could leverage progress in this field. To this end, we explore the use of statistical data assimilation (SDA) to identify solutions to a small-scale model of neutrino flavor transformation. SDA is a machine learning (ML) formula wherein a dynamical model is assumed to generate any measured quantities. Our example study seeks to infer the flavor transformation histories of two mono-energetic neutrino beams coherently interacting with each other and with a matter background. We require that the solution be consistent with measured neutrino flavor fluxes at the point of detection, and with constraints placed upon the flavor content at various locations along their trajectories, such as the point of emission, and the locations of the Mikheyev–Smirnov–Wolfenstein (MSW) resonances. We show how the procedure efficiently identifies solution regimes, and rules out regimes where solutions are infeasible. Overall, results intimate the promise of this "variational annealing" methodology to efficiently probe an array of fundamental questions that traditional numerical simulation codes render difficult to access.
Zoom link will be available via announcement email, or by contacting: stroberg[at]uw.edu.