Impact of higher-modes and merger modeling for GW150914 & GW170104

ORAL

Abstract

Gravitational-wave detectors have begun observing coalescences of heavy ($\gtrsim 50-60M_\odot$) binary black holes (BBH) at a consistent pace for the past few years. A high level of waveform template accuracy is required for unbiased and precise estimation of source parameters for such BBH signals. Numerical relativity (NR) continues to provide the most accurate waveforms, especially when it comes to capturing nonlinear general relativistic effects near merger and subdominant waveform modes. Recently developed NR surrogate models interpolate these NR waveforms over the BBH parameter space while preserving NR-level accuracy. They therefore facilitate direct application of NR information to Bayesian parameter inferencing on BBH signals for the first time without additional approximations. In this talk we present a re-analysis of the first two heavy BBH mergers, GW150914 & GW170104, with NR surrogates. While the impact of both higher-modes and improved merger modeling on the GW signal itself is small, we find that their inclusion can shift posterior densities substantially for various parameters, especially source location and orientation, as well as the effective spin of the binary.

Presenters

  • Prayush Kumar

    Cornell University

Authors

  • Prayush Kumar

    Cornell University

  • Jonathan Blackman

    California Institute of Technology

  • Scott E Field

    University of Massachusetts Dartmouth

  • Mark A Scheel

    Caltech, California Institute of Technology

  • Chad Galley

    Jet Propulsion Laboratory, California Institute of Technology

  • Michael Boyle

    Cornell University

  • Lawrence E Kidder

    Cornell University

  • Harald P Pfeiffer

    Max Planck Institute for Gravitational Physics, Canadian Institute for Theoretical Astrophysics, Max Planck Institute for Gravitational Physics

  • Bela Szilagyi

    California Institute of Technology, Jet Propulsion Laboratory

  • Saul A Teukolsky

    Cornell University, Caltech, Cornell University, Cornell University, California Institute of Technology