Parameter Estimation of Gravitational Waves from Nonprecessing BH-NS Inspirals with higher harmonics: Comparing MCMC posteriors to an Effective Fisher Matrix
R. O'Shaughnessy (1), B. Farr (2), E. Ochsner (1), Hee-Suk Cho (3), C., Kim (4), Chang-Hwan Lee (3) ((1) University of Wisconsin-Milwaukee, (2), Northwestern University, (3) Department of Physics, Pusan National, University, Korea, (4) Department of Physics

TL;DR
This study uses MCMC and Fisher matrix methods to analyze gravitational wave parameter estimation for nonprecessing BH-NS binaries, finding higher harmonics offer limited additional information at expected detection levels.
Contribution
It compares MCMC posteriors with an effective Fisher matrix approach for BH-NS inspirals, providing tools to assess potential new physics and parameter measurement accuracy.
Findings
Higher harmonics provide minimal extra information about source geometry.
Results favor the effective Fisher matrix approach for parameter estimation.
Masses can be measured with high confidence, constraining astrophysical models.
Abstract
Using the \texttt{lalinference} Markov-chain Monte Carlo parameter estimation code, we examine two distinct nonprecessing black hole-neutron star (BH-NS) binaries with and without higher-order harmonics. Our simulations suggest that higher harmonics provide a minimal amount of additional information, principally about source geometry. Higher harmonics do provide disproportionately more information than expected from the signal power. Our results compare favorably to the "effective Fisher matrix" approach. Extrapolating using analytic scalings, we expect higher harmonics will provide little new information about nonprecessing BH-NS binaries at the signal amplitudes expected for the first few detections. Any study of subdominant degrees of freedom in gravitational wave astronomy can adopt the tools presented here ( and ) to assess whether new physics is accessible…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
