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

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.