Parameter Estimation with Targeted Eccentric Numerical-Relativity Simulations for GW200208_22 and GW190620

ORAL

Abstract

We have analyzed LVK gravitational wave events that show some evidence of eccentricity from TEOBResumS modeling parameter estimations and have confronted them independently with full numerical generated waveforms from our bank of nearly 2000 simulations of binary black holes. We have used RIFT for Bayesian parameter estimation and found that GW200208_22 KDE estimates favor eccentricities e20= 0.191+0.123-0.174 upon entering the LVK band at 20 Hz within a 90% confidence limit. We employed 39 new targeted NR simulations and found a top improved likelihood lnL matching waveform, compared to model-based analysis, with an eccentricity at 20Hz, e20 = 0.200, thus reinforcing the eccentric hypothesis of the binary. We have also used our full bank of numerical waveforms on GW190620 finding that it favors eccentricities at 10 Hz in the range of 0 ≤ e10 ≤ 0.3. New specifically targeted simulations will be required to narrow this eccentricity range.

*This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. GF gratefully acknowledges the support of University of Calabria through a research fellowship funded by DR 1688/2023. COL gratefully acknowledges support from NSF awards AST-2319326, PHY-2207920 and PHY-2513442. ROS gratefully acknowledges support from NSF awards NSF PHY-1912632, PHY-2012057, PHY-2309172, AST-2206321, and the Simons Foundation.

Publication: The work has been submitted to PRD with the arxiv ID of arXiv:2507.22862

Presenters

  • Patricia McMillin

    • Rochester Institute of Technology

Authors

  • Patricia McMillin

    • Rochester Institute of Technology
  • Katelyn J Wagner

    • Rochester Institute of Technology
  • Giuseppe Ficarra

    • Rochester Institute of Technology
    • University of Calabria
  • Carlos O Lousto

    • Rochester Institute of Technology
  • Richard O’Shaughnessy

    • Rochester Institute of Technology