Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published May 15, 2022 | Submitted + Published
Journal Article Open

Probing neutron stars with the full premerger and postmerger gravitational wave signal from binary coalescences

Abstract

The gravitational wave signal emitted during the coalescence of two neutron stars carries information about the stars' internal structure. During the long inspiral phase the main matter observable is the tidal interaction between the binary components, an effect that can be parametrically modeled with compact-binary solutions to general relativity. After the binary merger the main observable is frequency modes of the remnant, most commonly giving rise to a short-duration signal accessible only through numerical simulations. The complicated morphology and the decreasing detector sensitivity in the relevant frequencies currently hinder detection of the postmerger signal and motivate separate analyses for the premerger and postmerger data. However, planned and ongoing detector improvements could soon put the postmerger signal within reach. In this study we target the whole premerger and postmerger signal without an artificial separation at the binary merger. We construct a hybrid analysis that models the inspiral with templates based on analytical calculations and calibrated to numerical relativity and the postmerger signal with a flexible morphology-independent analysis. Applying this analysis to GW170817 we find, as expected, that the postmerger signal remains undetected. We further study simulated signals and find that we can reconstruct the full signal and simultaneously estimate both the premerger tidal deformation and the postmerger signal frequency content. Our analysis allows us to study neutron star physics using all the data available and directly test the premerger and postmerger signal for consistency thus probing effects such as the onset of the hadron-quark phase transition.

Additional Information

© 2022 American Physical Society. Received 18 February 2022; accepted 20 April 2022; published 11 May 2022. We thank Will Farr and Wynn Ho for many useful discussions. We also thank Sophie Hourihane and Tyson Littenberg for discussions and assistance about BayesWave. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (https://www.gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. N. J. C. was supported by NSF Grant No. PHY1912053. M. W. gratefully acknowledges support and hospitality from the Simons Foundation through the predoctoral program at the Center for Computational Astrophysics, Flatiron Institute. The Flatiron Institute is supported by the Simons Foundation. A. B. acknowledges support by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 759253, by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 279384907—SFB 1245, by DFG—Project-ID 138713538—SFB 881 ("The Milky Way System", subproject A10) and by the State of Hesse within the Cluster Project ELEMENTS. Software: gwpy [173], matplotlib [174]. K. C. was supported by NSF Grant No. PHY-2110111.

Attached Files

Published - PhysRevD.105.104019.pdf

Submitted - 2202.09382.pdf

Files

PhysRevD.105.104019.pdf
Files (23.9 MB)
Name Size Download all
md5:0d3655ec9f31f05b792f8e1fe643e719
12.0 MB Preview Download
md5:0b736b0b5e1022c8e58f39012607aa09
11.9 MB Preview Download

Additional details

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