Degeneracy in the inference of phase transitions in the neutron star equation of state from gravitational wave data

Creators: Raithel, Carolyn A.; Most, Elias R.

Abstract

Gravitational wave (GW) detections of binary neutron star inspirals will be crucial for constraining the dense matter equation of state (EoS). We demonstrate a new degeneracy in the mapping from tidal deformability data to the EoS, which occurs for models with strong phase transitions. We find that there exists a new family of EoS with phase transitions that set in at different densities and that predict neutron star radii that differ by up to ~500m, but that produce nearly identical tidal deformabilities for all neutron star masses. Next generation GW detectors and advances in nuclear theory may be needed to resolve this degeneracy.

Additional Information

The authors thank Gabriele Bozzola, Katerina Chatziioannou, Pierre Christian, Phil Landry, Feryal Özel, Dimitrios Psaltis, Jocelyn Read, Ingo Tews, and Nicolas Yunes for insightful comments on this work. The authors gratefully acknowledge support from postdoctoral fellowships at the Princeton Center for Theoretical Science, the Princeton Gravity Initiative, and the Institute for Advanced Study. CAR additionally acknowledges support as a John N. Bahcall Fellow at the Institute for Advanced Study. This work was performed in part at the Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1607611. ERM acknowledges support for compute time allocations on the NSF Frontera supercomputer under grants AST21006. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) [98] through Expanse at SDSC and Bridges-2 at PSC through allocations PHY210053 and PHY210074. The simulations were also in part performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology's High Performance Computing Center and Visualization Laboratory at Princeton University. The authors also acknowledge the use of high-performance computing at the Institute for Advanced Study.

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