2021-12-29 15:00  P5A-1

[Journal Club] Translating neutron star observations to nuclear symmetry energy via artificial neural networks

Herlik Wibowo

In this talk, I will present a paper (arXiv: 2112.04089v1) written by Plamen G. Krastev, a theoretical nuclear physicist and astrophysicist at Harvard University. In this paper, the author introduces a novel method based on deep neural networks (DNNs) to directly extract the information of the nuclear symmetry energy as a function of density from observational neutron star data.
In my presentation, I will provide a brief overview of the equation of state (EOS), neutron star structure equations, tidal deformability, and deep neural networks. From this overview, I will discuss the method proposed by the author, the results, and, finally, the summary and outlook.