A design case study in which a course syllabus is reauthored into a singing-avatar video via an AI pipeline, with a claimed reproducible workflow and public code but no empirical evaluation.
Realistic Finite-Temperature Effects in Neutron Star Merger Simulations
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abstract
Binary neutron star mergers provide a unique probe of the dense-matter equation of state (EoS) across a wide range of parameter space, from the zero-temperature EoS during the inspiral to the high-temperature EoS following the merger. In this paper, we implement a new model for calculating parametrized finite-temperature EoS effects into numerical relativity simulations. This "M* model" is based on a two-parameter approximation of the particle effective mass and includes the leading-order effects of degeneracy in the thermal pressure and energy. We test our numerical implementation by performing evolutions of rotating single stars with zero- and non-zero temperature gradients, as well as evolutions of binary neutron star mergers. We find that our new finite-temperature EoS implementation can support stable stars over many dynamical timescales. We also perform a first parameter study to explore the role of the M* parameters in binary neutron star merger simulations. All simulations start from identical initial data with identical cold EoSs, and differ only in the thermal part of the EoS. We find that both the thermal profile of the remnant and the post-merger gravitational wave signal depend on the choice of M* parameters, but that the total merger ejecta depends only weakly on the finite-temperature part of the EoS across a wide range of parameters. Our simulations provide a first step toward understanding how the finite-temperature properties of dense matter may affect future observations of binary neutron star mergers.
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Designing Singing Syllabi with Virtual Avatars: AI-Assisted Syllabus Reauthoring
A design case study in which a course syllabus is reauthored into a singing-avatar video via an AI pipeline, with a claimed reproducible workflow and public code but no empirical evaluation.