Published June 20, 2017 | Version v1
Journal article

Probing Extreme-density Matter with Gravitational-wave Observations of Binary Neutron Star Merger Remnants

  • 1. Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540 (United States)
  • 2. Department of Mathematical, Physical and Computer Sciences, University of Parma, I-43124 Parma (Italy)
  • 3. Dipartimento di Fisica "Enrico Fermi," Università di Pisa, Pisa I-56127 (Italy)
  • 4. NSCL/FRIB and Department of Physics and Astronomy, Michigan State University, 640 S Shaw Lane, East Lansing, MI 48824 (United States)
  • 5. TAPIR, Walter Burke Institute for Theoretical Physics, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125 (United States)

Description

We present a proof-of-concept study, based on numerical-relativity simulations, of how gravitational waves (GWs) from neutron star merger remnants can probe the nature of matter at extreme densities. Phase transitions and extra degrees of freedom can emerge at densities beyond those reached during the inspiral, and typically result in a softening of the equation of state (EOS). We show that such physical effects change the qualitative dynamics of the remnant evolution, but they are not identifiable as a signature in the GW frequency, with the exception of possible black hole formation effects. The EOS softening is, instead, encoded in the GW luminosity and phase and is in principle detectable up to distances of the order of several megaparsecs with advanced detectors and up to hundreds of megaparsecs with third-generation detectors. Probing extreme-density matter will require going beyond the current paradigm and developing a more holistic strategy for modeling and analyzing postmerger GW signals.

Availability note (English)

Available from http://dx.doi.org/10.3847/2041-8213/aa775f

Additional details

Identifiers

Publishing Information

Journal Title
Astrophysical Journal Letters
Journal Volume
842
Journal Issue
2
Journal Page Range
[5 p.]
ISSN
2041-8205

INIS