Bayesian Inference and the Microphysics of Neutron Star Mergers
ORAL · Invited
Abstract
An accurate simulation of a merger of two neutron stars requires a diverse array of nuclear physics input. This input is collected into a model which itself must be constrained with nuclear experiment and multi-messenger observations. After the simulation is performed, additional modeling is often required to compute the multi-messenger observables. Finally, these observables can be compared with data in order to constrain the nuclear physics unknowns which are of interest. In this talk, I briefly review recent efforts in several different directions to improve these theoretical models, the simulations, and their interplay with simulation and multi-messenger observations.
*This work is funded by PHY 21-16686, PHY 21-03680, and AST 22-06322.
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Presenters
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Andrew W Steiner
- University of Tennessee