Bayesian Analysis of Neutron Star Data

Neutron stars are remnants of the cores of massive stars at the end of their life cycles. Once a massive star has depleted its fuel for fusion, it undergoes core collapse that creates a supernova and forms an extremely dense neutron star. Neutron stars are dense to the degree that the electrons in the atoms are squeezed into the nuclei and react with protons to become neutrons. In neutron stars, it is the strong force, one of the four fundamental forces of nature, that resists further collapse by gravity.

In nuclear physics, there are many proposed equations of state (EoS) for neutron stars based on theories. In order to find the true EoS for neutron stars, data from observation is needed. In recent years, the Neutron Star Interior Composition Explorer (NICER) telescope and the Laser Interferometer Gravitational-wave Observatory (LIGO) produced spectroscopy and gravitational wave data on neutron stars, from which mass, radius, and deformability can be calculated. This made it possible to constrain the candidates for the EoS.

In my project, I plan to analyze the three new NICER data sets to further constrain the EoS. I will write Python programs that pick out the EoS with high likelihood of observing this data, using Bayesian statistics.

Neutron stars provide an effective lab for studying matter and the laws of physics at extreme densities on the fringe of forming a black hole. Constraining the neutron star EoS improves our understanding of the interaction between the strong force and gravity.