Improving the future detectability and inference of binary neutron star post-merger signals with photon counting readout schemes

ORAL

Abstract

Modern designs of gravitational-wave interferometers rely on homodyne readout techniques to extract information about the observed gravitational-wave strain. However, these designs' sensitivities are limited by quantum shot noise from mid- to high-frequencies. Recent studies have presented the possibility of implementing filter cavities to directly count photons – thereby removing the presence of shot noise – that could be associated with the presence of a stochastic gravitational-wave background. In this study, we demonstrate the further utility of a photon counting readout scheme for the future of transient gravitational-wave astronomy by focusing on its application to binary neutron star post-merger detection and inference. We simulate an ensemble of realistic, low signal-to-noise ratio post-merger signals and compare inferences from both readout methods. We find that photon counting will significantly improve the chances of detection for the low signal-to-noise ratio post-merger signals expected during the era of third generation gravitational-wave detectors.

*This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. 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.

Presenters

  • Ethan Payne

    • LIGO Laboratory, Caltech

Authors

  • Ethan Payne

    • LIGO Laboratory, Caltech
  • Katerina Chatziioannou

    • Caltech
  • Lee McCuller

    • California Instititue of Technology