Presentation #105A.06 in the session Stellar & Compact Objects I.
Neutron star cores contain the densest matter in the observable universe. The state of this matter is of interest in numerous fields, including the study of supernovae and the understanding of nuclear physics more generally, but it cannot be explored in laboratory experiments. Therefore, observations of neutron stars are critical. Although the composition of the matter would be of great interest (e.g., is it primarily neutrons, or mainly hyperons, or free quark matter, or strange quark matter), macroscopic observables such as the neutron star mass-radius relation depend primarily on the equation of state (EOS), which for neutron star core matter is simply the pressure as a function of energy density: P (ε). As a result, measurements of quantities such as masses, radii, and tidal deformability can be used to constrain the EOS. Precise and reliable radius measurements would be particularly valuable. However, most attempts at radius measurements are susceptible to major systematic errors, meaning that the inferred radius can be significantly biased even if the fit to the data appears to be statistically good.
Previous studies suggested that radii inferred using new X-ray data provided by NASA’s Neutron Star Interior Composition Explorer (NICER) mission may be much more immune from such systematic errors. This is in part because, compared with previous measurements that obtained averaged spectra and fluxes, NICER adds an extra dimension of data when observing millisecond pulsars by timing the photons precisely (to better than 100 nanosecond resolution). Thus, it is possible to obtain the spectrum as a function of rotational phase and see variations such as heated regions on the star rotate into and out of view hundreds of times per second. This extra information seems promising to break degeneracies and mitigate systematic errors, but an in-depth study is necessary. Here we report the first steps of that study, in which we generate NICER-like synthetic data and determine the quality of fit and bias in radius obtained when we fit the data using a model different from the model used to generate the data.