Translating neutron star observations to nuclear symmetry energy via artificial neural networks

Understand

One of the most significant challenges involved in efforts to understand the equation of state of dense neutron-rich matter is the uncertain density dependence of the nuclear symmetry energy.

• Because of its broad impact, pinning down the density dependence of the nuclear symmetry energy has been a longstanding goal of both nuclear physics and astrophysics.
• Recent observations of neutron stars, in both electromagnetic and gravitational-wave spectra, have already constrained significantly the nuclear symmetry energy at high densities.
• Training deep neural networks to learn a computationally efficient representation of the mapping between astrophysical observables of neutron stars, such as masses, radii, and tidal deformabilities, and the nuclear symmetry energy allows its density dependence to be determined reliably and accurately.