Evolution of bare quark stars in full general relativity: Single star case

Authors: Enping Zhou, Kenta Kiuchi, Masaru Shibata, Antonios Tsokaros, Koji Uryu

Phys. Rev. D 103, 123011, June 2021
17 pages, 17 figures; accepted for publication in PRD (submitted to PRD in July 2020)

Abstract: We introduce our approaches, in particular the modifications of the primitive recovery procedure, to handle bare quark stars in numerical relativity simulations. Reliability and convergence of our implementation are demonstrated by evolving two triaxially rotating quark star models with different mass as well as a differentially rotating quark star model which has sufficiently large kinetic energy to be dynamically unstable. These simulations allow us to verify that our method is capable of resolving the evolution of the discontinuous surface of quark stars and possible mass ejection from them. The evolution of the triaxial deformation and the properties of the gravitational-wave emission from triaxially rotating quark stars have been also studied, together with the mass ejection of the differentially rotating case. It is found that supramassive quark stars are not likely to be ideal sources of continuous gravitational wave as the star recovers axisymmetry much faster than models with smaller mass and gravitational-wave amplitude decays rapidly in a timescale of $10\,$ms, although the instantaneous amplitude from more massive models is larger. As with the differentially rotating case, our result confirms that quark stars could experience non-axisymmetric instabilities similar to the neutron star case but with quite small degree of differential rotation, which is expected according to previous initial data studies.

Submitted to arXiv on 16 May. 2021

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.