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Published December 15, 2021 | Published
Journal Article Open

Impact of the nuclear symmetry energy on the post-merger phase of a binary neutron star coalescence

Abstract

The nuclear symmetry energy plays a key role in determining the equation of state of dense, neutron-rich matter, which governs the properties of both terrestrial nuclear matter as well as astrophysical neutron stars. A recent measurement of the neutron skin thickness from the PREX Collaboration has lead to new constraints on the slope of the nuclear symmetry energy, L, which can be directly compared to inferences from gravitational wave observations of the first binary neutron star merger inspiral, GW170817. In this paper, we explore a new regime for potentially constraining the slope, L, of the nuclear symmetry energy with future gravitational wave events: the post-merger phase of a binary neutron star coalescence. In particular, we go beyond the inspiral phase, where imprints of the slope parameter L may be inferred from measurements of the tidal deformability, to consider imprints on the post-merger dynamics, gravitational wave emission, and dynamical mass ejection. To this end, we perform a set of targeted neutron star merger simulations in full general relativity using new finite-temperature equations of state, which systematically vary L while keeping the magnitude of the symmetry energy at the saturation density, S, fixed. We find that the post-merger dynamics and gravitational wave emission are mostly insensitive to the slope of the nuclear symmetry energy. In contrast, we find that dynamical mass ejection contains a weak imprint of L, with large values of L leading to systematically enhanced ejecta.

Additional Information

© 2021 American Physical Society. We thank L. Jens Papenfort for providing low-density EoS matching routines. E. R. M. and C. R. gratefully acknowledge support from postdoctoral fellowships at the Princeton Center for Theoretical Science, the Princeton Gravity Initiative, and the Institute for Advanced Study. C. R. additionally acknowledges support as a John N. Bahcall Fellow at the Institute for Advanced Study. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. ACI-1548562. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper, under Leadership Resource Allocation Grants No. AT21006 and No. AT20008. Additionally, the authors are pleased to acknowledge that the work reported on in this paper was partially performed using the Princeton Research Computing resources at Princeton University which is a consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology's Research Computing. Part of this work was performed at the Aspen Center for Physics, which is supported by National Science Foundation Grant No. PHY-1607611. The participation of E. R. M. at the Aspen Center for Physics was supported by the Simons Foundation. Both authors have contributed equally to this work.

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Created:
August 20, 2023
Modified:
October 23, 2023