From data to dense matter: inferring neutron star physics using computational analyses

Abstract

Neutron stars host a range of interesting matter states from matter at supranuclear densities in their cores to possible neutron superfluidity and exotic nucleonic matter in their crusts. In this thesis, I use two distinct approaches probing different density and temperature regimes of the neutron star, probing the surface and crustal layers using X-ray data and the deep interior at high densities using gravitational waves. In the first part, we work on improvements to the theoretical numerical models of cooling neutron stars, which can be confronted with observations to infer interior properties. In particular, we investigate the effect of diffusive nuclear burning in the low-density envelope on cooling simulations and provide new analytic temperature relations. We find that a time-varying envelope composition should be taken into account and that the envelope can produce a non-negligible heat flux that would otherwise be interpreted as originating from the interior. In the second part, we develop a new computational gravitational wave data analysis method in order to simultaneously find and extract information about both the inspiral and post-merger remnant of a binary neutron star (BNS) coalescence. The code models the inspiral using solutions to general relativity whereas any unmodelled signal component is captured by sine-Gaussian wavelets. For the first time, we perform hybrid analyses of the full simulated BNS signal including both the inspiral and the post-merger component and find that some features are only extracted using the full analysis.

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Identifiers

ORCID for Wynn Ho: orcid.org/0000-0002-6089-6836

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