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Inference Offers a Metric to Constrain Dynamical Models of Neutrino Flavor Transformation by Armstrong, Eve; Patwardhan, Amol V.; Rrapaj, Ermal; Ardizi, Sina Fallah; Fuller, George M. is a scholarly article available to read on EtoBox.

What is Inference Offers a Metric to Constrain Dynamical Models of Neutrino Flavor Transformation about?

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, which augment and complement existing sophisticated computational tools, could leverage progress in this field. To this end we continue our exploration 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. Specifically, we use an optimization formulation of SDA wherein a cost function is extremized via the variational method. Regions of state space in which the extremization identifies the global minimum of the cost function will correspond to parameter regimes in which a model solution can exist. 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 requir

Author

Armstrong, Eve; Patwardhan, Amol V.; Rrapaj, Ermal; Ardizi, Sina Fallah; Fuller, George M.

Published

2020

Language

EN