Bulletin of the American Physical Society
2023 APS April Meeting
Volume 68, Number 6
Minneapolis, Minnesota (Apr 15-18)
Virtual (Apr 24-26); Time Zone: Central Time
Session K09: Gravitational Wave Parameter Estimation
3:45 PM–5:21 PM,
Sunday, April 16, 2023
Room: Conrad B/C - 2nd Floor
Sponsoring
Unit:
DGRAV
Chair: Colm Talbot, Massachusetts Institute of Technology
Abstract: K09.00002 : A Novel Method for Placement of Numerical Relativity Simulations Informed by NR-based Parameter Estimation
3:57 PM–4:09 PM
Presenter:
Jacob A Lange
(University of Texas at Austin)
Authors:
Jacob A Lange
(University of Texas at Austin)
Michael Boyle
(Cornell University)
Manuela Campanelli
(Rochester Institute of Technology)
Andrea Ceja
(California State University, Fullerton)
Deborah Ferguson
(University of Texas at Austin)
James Healy
(Rochester Institute of Technology)
Hector Iglesias
(University of Texas at Austin)
Aasim Z Jan
(University of Texas at Austin)
Lawrence E Kidder
(Cornell University)
Pablo Laguna
(University of Texas at Austin)
Carlos O Lousto
(Rochester Institute of Technology)
Geoffrey Lovelace
(California State University, Fullerton)
Erick Martinez
(University of Texas at Austin)
Ryan Nowicki
(University of Texas at Austin)
Richard O'Shaughnessy
(Rochester Institute of Technology)
Harald P Pfeiffer
(Max Planck Inst)
Mark A Scheel
(Caltech)
Deirdre M Shoemaker
(University of Texas at Austin)
Daniel Tellez
(California State University, Fullerton)
Saul A Teukolsky
(Cornell University)
Yosef Zlochower
(Rochester Institute of Technology)
On the 21st of May 2019, the LIGO-Virgo-KAGRA Collaboration detected a gravitational wave signal from a massive, highly precessing binary black hole merger. While all the analyses with different gravitation wave models recovered similar parameters, the peaks of the posterior distributions were noticeably different for models that include different physics. In this work, we present results for GW190521 from a method that suggests new numerical relativity simulations based on a parameter estimation analysis using only numerical relativity waveforms. This method attempts to place new simulations by taking into account the part of parameter space relevant for a given event as well as the sparse part of parameter space in the existing numerical relativity grid. We then add these new simulations into the NR-based analysis and quantify the impact of these targeted simulations by assessing the change in the posterior.
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